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Extended Abstracts

APPLIED GEOPHYSICS 1

1 Revealing a leakage model using dipole-dipole investigation and field mapping; A case study at HuaiYai

Reservoir, Petchabun Province, Thailand

Benjamas Sawatdipong, Anchalee Kongsuk

7 High Resolution Automatic 3D Off-set Pole-Dipole Resistivity Measurements for Deep Groundwater In-

vestigation

Desell Suanburi, Natthee Rongkhapimonpong, Channarong Thangkanasup

10 Landslide Risk Status of Road High Cutting Sandstone Slope by 2D Resistivity Imaging and Seismic

Refraction Technique

Songkiert Tansamrit, Desell Suanburi

14 The Integration of Ground and Underwater Resistivity Measuring for the Leakage of Internal Structure

at Gypsum Mine Boundary

Desell Suanburi, Wimonsiri Methaweranon, Monkon Ponchunchoovong, Boonyoung Tepsut

17 An Investigation of The Flood-Affected Concrete Structures Using Resistivity Measurements

Narongchai Wiwattanachang, Pham Huy Giao

23 Possibility of chemical contamination from waste-dumping area to irrigation canal-interpretation based

on geophysical data of an area in Mae Jo, Chiang Mai Provinces, Thailand

Noppadol Poomvises, Sarawute Chantraprasert

31 Application of geophysical methods for characterizing a selected solid waste disposal site in Songkhla

province

Thirat Sommai, Kamhaeng Wattanasen, Sawasdee Yodkayhun

36 Detection Leakage Reservoir located on Fault zone and Karst Topography by Dipole-Dipole Resistiv-

ity and Seismic refraction survey : A case study at Ban Phra Jaedee Sam Ong reservoir, Karnjanaburi

Province Thailand

Tirawut Na Lampang, Anchalee Kongsuk, Benjamart Sawaddipong, Noppadol Poomvises, Narucha Sangtong

CRUSTAL STUDIES 42

42 Fault Delineation Using Magnetic Data in the Eastern Part of Chiang Mai Basin

Chawanun Ninsom, Siripon Chaisri, Sarawute Chantraprasert

48 Geophysical Surveys to Detect Potential Active Faults in San Sai District, Chiang Mai Province

Tanapon Suklim, Suwimon Udphuay, Siriporn Chaisri, Sarawute Chantrapraserta

53 Thailand Crustal Thickness Estimation Using Joint Inversion of Surface Wave Dispersion and Receiver

Functions

Tira Tadapansawut, Siriporn Chaisri, Paiboon Nuannin

EARTHQUAKE STUDIES 62

62 Evaluation of TMD Seismograph Network Detection Capabilities

Chatupond Munkong, Paiboon Nuannin

68 Microtremor measurements in Chiang Mai city, northern Thailand for seismic microzonation

Narin Kluntong, Passakorn Pananont

71 Resistivity imaging to detect the liquefaction induced by the Mw 6.8 earthquake in Myanmar on March

24, 2011 in Chiang Rai province, northern Thailand

Rapeeporn Sakulnee, Passakorn Pananont

The 6th International Conference on Applied Geophysics, Nov 15-17, 2012, Kanchanburi, Thailand i

75 Micro-tremor in Bangkok and its comparison with amplified shear waves and H/V spectrum of Rayleigh

waves

Satoshi Morio, Yoshinori Kato, Akira Kitazumi, Suwith Kosuwan, Sitirag Limpisawad, Tirawat Boonyatee

GEOPHYSICAL MODELING AND INVERSION 82

82 Inversion of Magnetic Data from Remanent and Induced Sources

Robert Ellis, Barry de Wet, Ian Macleod

87 Extracting shear wave velocity from seismic reflection data: Case studies in near surface characterization

using Multichannel Analysis of Surface Wave (MASW)

Sawasdee Yordkayhun, Aksara Mayamae, Preeya Srisuwan

94 Quality Improvement Comparison Between Time-Space Window Varying Median Filter and Time Win-

dow Varying Median Filter

Siriphon Somsri, Pisanu Wongpornchai

GEOPHYSICAL FOR PETROLEUM EXPLORATION 100

100 Gas reservoir detection using three dimensional seismic attribute analysis, Gulf of Moattama, Offshore

Myanmar

Soe Linn Htike, Pisanu Wongpornchai

108 Porosity and Permeability Estimation from Seismic Attributes by Multi-layer Feedforward Neural Net-

work Technique in an Area of Gulf of Thailand

Theerachai Norkhamboot, Pisanu Wongpornchai

GEOTHERMAL EXPLORATION 112

112 Model-based Inversion of Magnetotelluric (MT) Data in the Fang Basin

Khin Moh Moh Latt, Pham Huy Giao

117 Geological Structures related to Hot Springs in Krabi, Southern Thailand

Usa Nilsuwan, Helmut Durrast

LABORATORY GEOPHYSICS 129

129 Geomechanical Simulation of Deformation by CO2 Injection into Homogeneous Sandstone

Avirut Puttiwongrak, Toshifumi Matsuoka

138 Dating Geological Events using Thermoluminescence Technique

Prakrit Noppradit, Sommai Changkian, Helmut Durrast

Index of Authors 143

The 6th International Conference on Applied Geophysics, Nov 15-17, 2012, Kanchanburi, Thailand ii

Revealing a leakage model using dipole-dipoleinvestigation and field mapping; A case study at HuaiYaiReservoir, Petchabun Province, Thailand

Benjamas Sawatdiponga,∗, Anchalee Kongsuka

a Geophysics group, Geology section, Office of Topographical and Geotechnical Survey, Royal Irrigation Department, Dusit,

Bangkok, Thailand∗, E-mail: [email protected]

ABSTRACT

The geophysical investigation of HuaiYai Reservoir, Petchabun Province aims to investigate critical area, delineate the leak path, and

analyze causes of leakage. The leakage at HuaiYai was taken place near outlet of saddle dam and the vicinity area. Field mapping was

initially mapped the geological structure and condition of joints in the area. Dipole-dipole imaging had later been used to map 2-D profiling.

Three survey lines were designed covering an area of problem in three levels. Line A, at +221.7 metres above mean sea level and total length

of 560 meters was located at a center line of saddle dam crest. Line B, at +204.4 m msl and total length of 340 meters, was at downstream

toe-drain and approximately 3 metres higher than control outlet. Line C was at front of control outlet building at +201.3 m msl, and the

total length of 365 meters. As the results of line A, 2-D profile shows three dominant anomalous zones of low resistivity range, station

235.5 m spheroid in shape, 235.5-355 m slightly dipping along the contact of saddle and spur, and 350-440 m, approximately 10 meters

thick, laying horizontal continuous on top of the spur. The 2-D resistivity profile of line B and C demonstrates seven anomalies between

station 0 to 340 with several shape, geometry, dimension, elevation, and resistivity range. The most anomalous zones likely appear beneath

the river outlet. Combine resistivity imaging and geological mapping, it can be interpreted cause of the leakage into three assumptions.

First, the seepage flow and leak through main fracture inside tuffaceous sandstone foundation which is aligned in the northeast-southwest

direction by mechanism of gravity. Second, seepage from water run-off along downstream slope and under rip-rap layer. Apart of the water

seeped down under the gutter, which was designed to protect control outlet building, and leak out to cut slope which is located behind the

outlet building. Last, the ground surface behind control outlet building is approximately 30 centimeter lower than a shallow ground water

table of the area that make water flow out on cut-slope face as a slope seepage. A little while after, a maintenance team had treated the area

of problem with several methods to be safe until the present day.

KEYWORDS: Dipole-dipole electrical resistivity, Revealing leakage model, Leakage, HuaiYai Reservoir, geophysical investigation,

resistivity imaging

INTRODUCTION

Huai Yai Dam, project initiated by His Majesty King Bhu-

mibol Adulyadej for development of water sources, agricul-

ture, environment, occupational promotion and public health

located, located in Amphoe Muang Petchabun province of

Thailand (Figure 1), as a big barrier obstructing water from

HuaiYai Gully as a tributary of Pa-Sak river. The project

consists two parts. The first one is a main dam with a

crest height of 34 m, total length of 370 m and the second

one is a saddle dam with a crest height of 14 m, and total

length of 285 m. The catchment area is approximately 13.27

million cubic meters whereas an irrigation area is 18 square

kilometers. The general layout of the dam site is shown in

Figure 2.

The Huai Yai Dam was constructed in 2005 and finished

in 2010. The seepage problem were first observed on January

14th, 2012, the first year of water impounding, when the

reservoir water level was +213.05 m msl and volume of water

in reservoir was 10.04 million cubic meters. The leakage

was taken place near outlet of saddle dam, the vicinity area

and seepage through along road at downstream toe-drain

near outlet of saddle dam. Later on March 26th, 2012, the

water level was lowered to an elevation of +208.17 m msl,

as volume-averaging of water in reservoir was 5.6 million

cubic meters. The seepage has been decreased. Anyhow, new

failure occurred at gutter that collapsed total length of 20 m

and compacted soil along cut-slope of outlet was transported

The 6th International Conference on Applied Geophysics, Nov 15-17, 2012, Kanchanburi, Thailand 1

Leakage model from resistivity image

Figure 1 Location map

Figure 2 Generalized layout of the dam site

out with failure body moving down into the gutter behind

control outlet building and increased more over. In May

2012, a series of investigation was planned to find out the

position and the causes of the seepage. Field Mapping and

the geophysical investigations, such as resistivity and seismic

refraction method were carried on.

METHOD

FIELD MAPPING

Detailed geological study of the dam site was performed

during the project preparation period with drilled-hole infor-

mation by Royal Irrigation Department before 2005. Report

shows that there are five rock units existing in the study area

as follows;

(i) Alluvial sediment (upper most unit)

(ii) Siltstone/shale ; left saddle km 0+000

(iii) Tuffaceous sandstone; km 0+400 at spur between main

dam and saddle

(iv) Fine sandstone; at spillway and river outlet control

building of main dam

(v) Chert unit; (lower most unit) at palaeo-channel river

Field survey in this study was focused at 5 stations and

pointed out that the control geologic structure covering this

area is a monocline structure with strike direction of NNE-

SSW and dipping direction 30-60 degree to SE. There are

three dominant sets of joint as N45W, N80W, and N5W.


Sawatdipong and Kongsuk

DIPOLE-DIPOLE ELECTRICAL RESISTIVITY

METHOD

The electrical resistivity method has been used in geotech-

nical and environmental investigation for about a century.

Fresh rock in general has a significantly higher resistivity

than clayey soil because it has much smaller primary porosity

and fewer interconnected pore spaces. clayey materials tend

to hold more moisture and have a higher concentration of ion

to conduct electrical, therefore, have resistivity values less

than 100 ohm-m (Telford and others 1990).

Figure 3 is shown the data collection sequence for the

dipole-dipole array in an investigation. The symbol ‘a’ de-

notes the unit spacing of electrodes, which is selected based

on the desired depth of penetration, the required resolution,

and the type of array. The electrode spacing and dipole

separation are constant for each traverse (n) and increase with

each successive traverse. Larger electrode spacing provides

data from greater depths, but with lower resolution.

Figure 3 The data collection sequence for the dipole-dipole array

in the investigation

Dipole-dipole electrical resistivity is one of geophysical

technique method has been used in data collection and used

for revealing leakage model. Electrical resistivity data was

collected from three survey lines. The lines were designed

covering an area of problem in 2 spacing system 5- and 2.5-

metre system.

For the first system, there was three lines arranged in

three different levels. Line A, at +221.7 metres above mean

sea level , total length of 560 meters, was located at a center

line of saddle dam crest. Line B, at +204.4 m msl and total

length of 340 meters, was at toe drain and approximately

3 metre higher than control outlet. Line C was at front of

control outlet building at +201.3 m msl,the total length of

365 meters (Figure 4 and 5).

For the last system, line D total length of 117.5 meters

was at downstream toe-drain with same level of line B

whereas line E total length of 117.5 meters was overlaid

with line C. The electrode spacing of 2.5 m was used to

provide a higher resolution data with the shallow depth of

investigation.

The acquisition data was later processed to generate 2-

Dresistivity models by using RES2DINV software, Geotomo

software, Malaysia, for showing a distribution of apparent

resistivity values.

SEISMIC REFRACTION METHOD

Refraction surveys have been used to estimate the velocity

structure of bedrocks , the depth to the surface bedrock, and

the extent of overburden soil. The basis of the interpretations

is the difference in the physical properties of the materials

and the underlying sediments or bedrock that result in differ-

ent seismic velocities (Abramson et al., 2002). Intercept-time

and reciprocal methods of interpreting refraction data can be

used to model velocity structures of the study areas. Seis-

mic refraction data were interpreted by calculated refractor

depths using overlapping refraction arrival times from both

forward and reverse shots.

The survey line B total length of 340 meters was

recorded with recurrent movement along with the resistivity

survey line B at downstream toe-drain and approximately 3

meters higher than control outlet. The survey line C total

length of 365 meters was recorded with recurrent movement

along the road at downstream toe-drain near control outlet

building (Figure 4 and 5). Geophone spacing along every

line was used a 5-m system.

RESULTS

The results and the interpretation of Dipole-dipole electrical

resistivity are shown in the Figure 6 and 7 while seismic

refraction lines are overlaid on only line B and C (Figure 6).

The profile of model resistivity on line A shows three domi-

nant anomalous zones of low resistivity range as follows;

(i) The anormalous zone X appeared at the middle of station

235.5 (an elevation +213 m a.s.l) is interpreted as com-

pacted soil of saddle dam. It is spheroid in shape, slightly

dipping toward the northwest, continuous downward

into the bedrock, and is located approximately 8 meters

from outlet (the middle of outlet is station 224.5 and an

elevation +198 m msl)

(ii) The anormalous zone Y appeared at the middle of station

235.5 to 355 (an elevation +205 to +220 m asl) is

interpreted as the zone of boundary between saddle dam

and spur. It is vertically continuous into the bedrock and

slightly dipping.

(iii) The anormaly zone Z appeared at station 350 to 440 at

top of saddle. It is laterally continuous, total thickness

approximately 10 meters and is interpreted as the weath-

ered layer of tuffaceous Sandstone.

The profile of model resistivity on line B and C show

seven dominant anomalous zones of low resistivity range

(Figure 6) at station 000 to 340 m that corresponding to

several shape, geometry, dimension, elevation, and resistivity

range. The most anomalous zones likely appear beneath the

outlet pipe.



Figure 4 Geophysical survey line (top view). Red color denotes 5-m survey while blue color donates 2.5-m system.

Figure 5 Geophysics Survey Line (Looking upstream SE)

The profile of modeling resistivity on line D and E were

also founded seven anomalies (Figure 7). Because of the

electrode spacing along line was 2.5 m, it can provide higher

resolution data therefore the results in Line D and Line E

well confirm that the anomalous zones in the surrounding

area clearly appear beneath the outlet.

CONCLUSIONS

The comparison and combination of results between differ-

ent geophysical methods and geological mapping yield the

conclusions of the 3 possibility of the leakage including thefollowing:

(i) The seepage flow to emerge and to leak through main

fracture in tuffaceous sandstone which is aligned in the

northeast-southwest direction. The leakage is conducted

through fracture by mechanism of gravity.

(ii) Surface runoff is controlled by downstream slope. As

part of the water from under rip-rap layer and seeped

down under the gutter that leak out to cut slope which

was designed to protect control outlet building, and is

located behind the outlet building. Cut-slope with clayey

materials components constantly in high water saturation

which causing soil internal friction angle and loss in


Sawatdipong and Kongsuk

Figure 6 Showing modeling resistivity in Line A Line B and Line C

Figure 7 Showing modeling resistivity in Line D and Line E, electrode spacing along line was 2.5 m



bond strength which were made to facilitate the failure.

(iii) The addition of water from the spur, the slope surface

of downstream dam or surface runoff which has the

groundwater higher level than a constant level. Clayey

materials tend to hold more moisture while have a higher

concentration of water. Finally it cannot reserved water

within aggregate of soil and then water leak out to the

cut-slope behind the control outlet building. Due to

the ground level of the control outlet building that is

located the steady level was approximately 30 cm from

the surface that particularly lower than a shallow ground

water table of the area of downstream dam.

REFERENCES

AIT, 1992. Short course on rock slope engineering.

Matsubara, Y., Kudo, H., Nakano, T., & Takeuchi, T., 1988. Lecture

notes for Advance Course On Seismic Surveys for Geotecnical

Engineering Investigation, Engineering Development Division,

Irrigation Engineering Center.

Ministry of Construction, 1992. Seismic Prospecting by OYO Cor-

poration, International Institute of Seismology and Earthquke

Engineering, Building Reserch Institute.

Sedat, T., 2002. Seepage problem in the karstic limestone foun-

dation of the kalecik dam (south turkey), Journal of Engineering

Geology, 36, 247–257.

Sharma, P., 1997. Environmental and Engineering Geophysics,

Cambridge UniversityPress, Cambridge.

Telford, W., Geldart, L., & Sheriff, R., 1990. Applied Geophysics,

Cambridge University Press.

Whiteley, 1984. Shallow Seismic Refraction Methods in Explo-

ration and Engineering, Univercity of New South Wales.

Zhou, W., Berk, B., & Stephenson, J., 2000. Reliability of dipole-

diploe electrical resistivity tomography for defining depth to

bedrock in covered karst terranes, Journal of the Environmental

Geology, 39, 760–766.


High Resolution Automatic 3D Off-set Pole-DipoleResistivity Measurements for Deep GroundwaterInvestigation

Desell Suanburia,∗, Natthee Rongkhapimonpongb, Channarong Thangkanasupc

a Department of Earth Sciences, Faculty of Science, Kasetsart Universityb Issara Mining Limited, Thailandc Suwanwajokkasikit Field Corp Research Station, Kasetsart University

∗, E-mail: [email protected], [email protected]

ABSTRACT

Due to a large amount of groundwater use for agricultural purpose, high yields and deep resources are needed to investigate effectively. An

application of a modified technique from an off-set pole-dipole array approach was performed at Suwanwajokkasikit Field Corp Research

Station, Kasetsart University, Nakhonratchasima province, where local hydro-geology aspects presented as limestone aquifer regions.

Objectives of true 3D resistivity measurement are to explore deep groundwater resource concisely with more than 160 m deep covering

an area of 300 m x 460 m by allowing for fast data acquisition with 48 electrode automatic reading of large quantity of data. Location

of 3D measurement was selected from previous 2D resistivity imaging. For survey specification, one set of measurement contains two

reading survey lines with electrode spacing of 20 m and line separation of 100 m while 17 current points are located at the middle between

reading survey lines with spacing of 40 m. Remote current electrode was positioned away 1000 m perpendicular to survey line direction.

Three measuring set were done in east-west direction. 3D inversion geo-electrical models were created by RES3DINV software package.

The result displays clearly that concise low resistivity zones appears within major high resistivity region which may infers to groundwater

zones in fracture or cavity in limestone at depth of 150 m to 180 m. Both shallow and deep groundwater zones can be classified and

located for future groundwater management in agriculture use. This approach can be proved as a new tool for effective deep groundwater

investigation.

KEYWORDS: Off-set pole-dipole, Deep groundwater, 3D Resistivity imaging

INTRODUCTION

Groundwater resources play as a significant rule for water

supply in agricultural uses during summer time in Thailand.

Groundwater system and it’s potential zones at agricultural

land are necessary to identify high yield of groundwater

boundary.

2D resistivity imaging were applied for groundwater

investigation successfully. (Suanburi, et. al., 2007) To

improved more effective achievement, a modified proce-

dure called “scanning technique” was introduced (Suanburi,

2010).

3D resistivity measurement have been attempted to in-

vestigate subsurface geological aspects with higher resolu-

tion and deeper position than previous 2D resistivity imaging

results.

Aims

The purpose of 3D resistivity measuring offset Pole-Dipole

configuration are to investigate for deep groundwater re-

sources concisely with more than 160 m deep covering an

area of 300 m × 460 m by allowing for fast data acquisition

with 48 multi-electrode automatic readings.

Location of study area

The study area is located in Suwanwajokkasikit Field Corp

Research Station, Kasetsart University, Nakhonratchasima

province. The boundary of the project area is covered by

749900E and 750550E, and 1620000N and 1620300N (see

Figure 1).

RESISTIVITY SURVEYING

Four 460 m survey lines were located by following the result

of previous 2D resistivity imaging which displays as limited


3D off-set pole-dipole for groundwater investigation

Figure 1 Location map and electrode configuration

depth and less detailed measuring points. Then three 3D

Offset Pole-Dipole set up were designed in E-W direction

covering an area of partly Suwanwajokkasikit Field Corp

Research Station. (See the position of survey lines in Figure

1).

Remote current electrode was positioned away 1000 m

perpendicular to survey line direction. The measuring sys-

tem, Offset Pole-Dipole electrode configuration, was used for

continuous and detailed subsurface investigation (explained

in the Figure 2). Measurements of data display as apparent

resistivity value by section and plan view form were carefully

interpreted in term of hydro-geological aspects. Then the

data were further compiled by RES3DINV software package

which created 3D inversion geo-electrical models.

Figure 2 Geometry and formula of generic four-electrode configu-

ration (Johnson, et.al., 2003)

RESULTS

As seen from 2D inversion model (Figure 3) and 3D models

(Figure 4 and 5) high resistivity zones (red color) may rep-

resented as limestone bedrock, are mainly found the whole

area from the surface to 140 m depth. High potential of

groundwater zone can be pinpoint as low resistivity zones

(blue color) which classify as shallow and deep aquifers

zones. Geological structure, e.g. fracture, fault and cavity

appearing in limestone, may infer as high yield of ground

water boundary.

Figure 3 A section of 2D resistivity imaging model

For one set of offset Pole-Dipole Resistivity measure-

ment, there are two reading survey lines with electrode

spacing of 20 m and line separation of 100 m while 17 current

points are located at the middle between reading survey lines

with spacing of 40 m.

CONCLUSIONS

The modified resistivity measuring technique using 48 auto-

matic multi-electrode instrument was successfully attempted

to apply 3D Offset Pole-Dipole method to investigate deep

ground water resources at the constrain area of 300m×460m.


Suanburi et al.

Figure 5 Sections of Resistivity models in E-W direction

Figure 4 Resistivity models in plan view at different depth

Geological structure location obtained 2D and 3D inversion

models, may correlate to aquifer zones.

REFERENCES

Denne, R., Collins, S., Brown, P., & Hee, R., 2001. A new survey

design for 3D IP inversion modelling at Copper Hill, in Extended

Abstracts of ASEG 15th Conference and Exhibition, Brisbane.

Johnson, W. J., 2003. Applications of the electrical resistivity

method for detection of underground mine workings, in Pro-

cessding of Workshop on Geophysical Technologies for Detecting

Underground Coal Mine Voids, Lexington, KY.

Suanburi, D., 2010. Resistivity scanning technique: A new

approach for effective groundwater investigation, in Proceeding

of the 5th International Conference on Applied Geophysics 11-13

November 2010 Phuket Thailand, Phuket Thailand.

Suanburi, D., Sommanut, B., & Leesumpan, P., 2007. Application

of 2D resistivity imaging techique at low potential site, in

Processding of Ground water Symposium 2007, p. 12, (in Thai).


Landslide Risk Status of Road High Cutting SandstoneSlope by 2D Resistivity Imaging and Seismic RefractionTechnique

Songkiert Tansamrita, Desell Suanburib,∗

a Energy Foundation, PTT Public Company Limitedb Department of Earth Sciences, Faculty of Science, Kasetsart University


ABSTRACT

The occurrence of large landslide hazards at Ban Na Tham Community, Tha U Thae Subdistrict, Kanchanadit District, Surat Thani Province

on March 2011 by factor of high rain fall of 996 ml within two days, revealed as various landslide forms e.g. deep seated, shallow and

surface landslides, and cave collapse affected from both granite and limestone regions, has been widely damaged Ban Na Tham watershed

area. The collapse of only one access road to Ban Na Tham Community where show as high cut slope road detached Ban Na Tham

community people from outside world for more than a week. The applications of resistivity imaging with dipole-dipole and Schlumberger

array, are to investigate subsurface geological structure of the current access road and to identify landslide risk status of access road

foundation. 2D resistivity measurement was performed with 600 m long, 5 m electrode spacing, and depth of 30 m, a long road side

direction covering landslide risk portion. Depth of bedrock was found varying from 1 m to 20 m. Various fault and fracture zones appear in

bedrock. Selecting high risk landslide location, further three 2D resistivity survey lines with dipole-dipole array reading and 5 m spacing

and 235 m long, were assigned in direction of cross cutting slope (with slope of 45-80%), perpendicular to road direction. Main fault

lines are found at back slope portion. Several fracture zones can be seen at shallow upside sandstone bedrock where the infiltration of

groundwater flows into underneath road position. 20 m thick and very moist colluviums/talus layer appears at underneath road presenting

high risk zone. VES data 1D inversion models were created to support subsurface interpretation. Seismic refraction measurement was

attempted along road side at high risk position. The thickness of low velocity zone (or depth of bedrock) coincide the result of resistivity

interpretation. The part of access road at deep bedrock is realized as very high risk of deep seated landslide. To prevent landslide occurrence

at this location, engineering foundation work is needed to maintain by draining groundwater.

KEYWORDS: Landslide risk, high cutting slope, 2D resistivity, seismic refraction

INTRODUCTION

The occurrence of large landslide hazards at Ban Na Tham,

one of the best conservation communities, Tha U Thae

Subdistrict, Kanchanadit District, Surat Thani Province on

March 2011 was affected by factor of high rain fall of more

than 996 ml within two days. There are various landslide

forms e.g. deep seated, shallow and surface landslides,

and cave collapse affected from both granite and limestone

regions. Houses and agricultural land uses were widely im-

pacted covering Ban Na Tham watershed area. The collapse

including partly Debris and rocks fall of only one access road

to Ban Na Tham Community where show as sandstone high

cut slope road, detached Ban Na Tham community people

from outside world for more than a week. Geophysical

investigating was needed to support engineering work for

maintaining road foundation.

Aims

The Applications of geophysical prospecting by 2D resistiv-

ity imaging and seismic refraction are to investigate subsur-

face geological structure and groundwater flow alignment at

the new constructed road which will be aware of landslide

risk status and of repeated landslide occurrences.

Location of survey area

The survey area is located along the access road to Ban Na

Tham Community with the area of 778700E - 759000E and

998100N - 998650N (see Figure 1).


Landslide risk status by 2D resistivity and seismic refraction

Figure 1 Location map of survey area

Figure 2 Location of survey lines

RESULTS

GEOPHYSICAL SURVEYING

Line A is located along road side (see in figure 2) covering

landslide risk portion. with 600 m long where conducting 2D

Figure 3 Interpretation of Line A along the road, found 3 locations

of high risk of landslide

Figure 4 (a) Interpreted geological section of Line 1 from resistiv-

ity and (b) from chargeability showing high risk of landslide beneath

road cutting.

resistivity multi-electrode measurement with 5 m electrode

spacing, and depth of 30 m. From initial data interpretation

along the road, it is found that there are three zones to be

high risk of landslide condition i.e. deep bedrock filled with

groundwater, and weathered zone at fracture or fault zone.

Then 3 survey lines (Line 1, 2 and 3) were positioned with

the direction of up-down slop (of 30 - 70%), crossing the

road. Both Dipole-Dipole and Schlumberger array reading

with 235 m long and electrode spacing of 5 m, were applied

for 2D section and 1D inversion model. Seismic refraction

measurements were conducted along the road side following

the result of Line A, with geophone spacing of 5 m.

The result of 2D inversion model of Line A displays

that depth of bedrock varies from 1 m to 20 m and various

fault and fracture zones appear in bedrock. (Figure 3) 2D

resistivity section of Line 1 (Figure 4(a)) presents low re-

sistivity zones (moisture/groundwater content) beneath road

position with bedrock depth of about 20 m. Main fault line

clearly found at cutting slope side. Chargeability anomalies

(Figure 4(b)) appear at between geological structure zones

may strengthen moist rock fragments/debris deposits (may

fill with clay enrichment). 1D inversion models (Figure

5) of Line 1 verify moisture portion (blue color) at differ-

ent depth from ground surface due to the discontinuity of

rock/groundwater layer affected from fault lines. 3D visual

presentation in Figure 6 for 4 Lines of resistivity imagings


Tansamrit and Suanburi

Figure 5 1D inversion models of Line 1 presenting low resistivity or groundwater content zone (blue color)

Figure 6 3D presentation of 4 interpreted sections

shows groundwater zones in both direction.

CONCLUSIONS

The application of resistivity and induced polarization imag-

ing including seismic refraction technique can be proved

for landslide protection by indicating subsurface features

which plays as critical conditions for road high cutting slopefoundation problems.

REFERENCES

Denne, R., Collins, S., Brown, P., & Hee, R., 2001. A new survey

design for 3D IP inversion modelling at Copper Hill, in Extended

Abstracts of ASEG 15th Conference and Exhibition, Brisbane.


Landslide risk status by 2D resistivity and seismic refraction

Johnson, W. J., 2003. Applications of the electrical resistivity

method for detection of underground mine workings, in Pro-

cessding of Workshop on Geophysical Technologies for Detecting

Underground Coal Mine Voids, Lexington, KY.

Suanburi, D., 2010. Resistivity scanning technique: A new

approach for effective groundwater investigation, in Proceeding

of the 5th International Conference on Applied Geophysics 11-13

November 2010 Phuket Thailand, Phuket Thailand.

Suanburi, D., Sommanut, B., & Leesumpan, P., 2007. Application

of 2D resistivity imaging techique at low potential site, in

Processding of Ground water Symposium 2007, p. 12, (in Thai).


The Integration of Ground and Underwater ResistivityMeasuring for the Leakage of Internal Structure atGypsum Mine Boundary

Desell Suanburia,∗, Wimonsiri Methaweranona, Monkon Ponchunchoovongb, Boonyoung Tepsutb

a Department of Earth Sciences, Faculty of Science, Kasetsart Universityb SCG Cement Co., Ltd


ABSTRACT

Operating gypsum mines often cope with the occurrence of internal structure leakage at mining boundary affected from surrounded abandon

gypsum mines which make trouble in mining activity management. SCG area 4 gypsum mine located at Thungthong sub-district, Nongbua

district, Nakornsawan province, the northern portion of the Nakhonsawan-Phichit Gypsum deposit region, will be re-operated for mining.

One corner the mine boundary (two boundary sides) appears adjacent to large and high level mining-water abandon gypsum mine. Then

the application of an integration of both on ground and underwater 2D resistivity reading continuously through the whole section was

performed to inspect the leakage status of subsurface boundary in both side including the mine edge part. Five survey lines, 3 lines in E-W

and 2 lines in N-S directions, with 10 m line separation and 5 m electrode spacing, were located cover on land and further on water surface.

60 multi-electrode equipment was introduced with automatic reading. Stainless steel electrodes were used for ground reading for 250 m

long. Sealed 10 copper electrode (water proof design) cable with 5 m spacing was positioned as floating 50 m long cable with allowing

copper electrodes submerged. Dipole-Dipole array (for 2D inversion model) and Schlumberger array (for modified processing and creating

1D inversion model) were used for all reading. 2D resistivity reading was successfully carried out to obtain nice natural continuous data

set. Shallow high resistivity zones found at the Eastern and Northern parts of survey area, are presented as gypsum zones. Clastic layer

can be mapped at the edge zone with thickness of more than 30 m and dimension of 200 m×100 m. Shallow low resistivity layer with

2-3 m thick found at depth of about 10m, was suppose as saturated infiltration of water from beside abandon-mine. Vertical narrow low

resistivity was found at the eastern side which may presented as high risk of the leakage point which needed to manage blockade before

mining activity at gypsum area 4.

KEYWORDS: Off-set pole-dipole, Deep groundwater, 3D Resistivity imaging

INTRODUCTION

Gypsum mine located at Thungthong sub-district, Nongbua

district, Nakornsawan province, the Northern portion of the

Nakornsawan-Phichit Gypsum deposit region will be re-

operate for mining. One corner of mine boundary was

surrounded by water fill from abandon mine. It should be

inspect for status of leakage at gypsum mine. Resistivity can

be applied for the leakage in underground (Ramirez, et al.,

1996).

This study have developed a new challenge tech-niques

and modified measuring instrument for integration ground

and underwater. 2D resistivity reading will be performed

as continuously through the whole part of mine boundary

corner. This may help to manage or protect the leakage of

internal structure at gypsum mine area 4 (of SCG mining)

before mining activity begin.

Aims

The purpose of combined both ground and under water

resistivity reading is to locate the zone of high risk leakage

internal structure which may prevent serious hazard of the

leakage at mine boundary.

Location of study area

The study area is located at Thungthong sub-district, Nong-

bua district, Nakornsawan province. The boundary of this

survey area is covered by 685800E-686300E and 1765450N-

176600N (see Figure 1).


Leakage boundary from ground and underwater resistivity

Figure 1 Location map and survey lines

RESISTIVITY SURVEYING

There are five 300 m survey lines i.e. 3 N-W survey lines

and 2 E-W survey lines with electrode spacing of 5 m and

line separation of 10 m. For under water elec-trode set up, 10

electrode (sealed copper electrode ca-ble) floating on water

in E-W direction and 20 elec-trodes floating on water in N-

W direction.

2D resistivity measuring with continuous reading whole

part of boundary mine corner, were performed in Dipole-

Dipole and Schlumberger array on ground and underwater.

Data obtained from Dipole-Dipole array were processed

as 2D section while data from Schlumberger array were used

in 1D inversion model to supported 2D model.

Figure 2 Integration of ground and under water resistivity measur-

ing

Figure 3 Ground cable (left) and Floating cable (right)

RESULTS

2D resistivity profiling can be explained the internal structure

of gypsum mine. Thickness of topsoil approximately 3m and

gypsum zone deep about 40m. The Northern and Eastern

parts of survey area are presented as gypsum zones presented

in high resistance. The channel of low resistivity (2-5 Ωm)

in vertical narrow was found at the edge of mine which may

presented as high risk of the leakage point

Figure 4 Ground survey line(left) and water survey line(right)


Suanburi et al.

Figure 5 2D resistivity profiling of N-W direction (upper) and E-W direction(lower)

Figure 6 1D inversion support 2D profiling

CONCLUSIONS

The applied 2D resistivity technique for combining ground

and underwater was successful for identified the leakage area

of boundary at both side of mine edge part.

REFERENCES

Ramirez, A., W., D., Binley, A., LaBrecque, D., & Roelant,

D., 1996. Detection of leaks in underground storage tanks

using electrical resistance methods, Journal of Engineering and

Environmental Geophysics, 1, 189–203.


An Investigation of The Flood-Affected ConcreteStructures Using Resistivity Measurements

Narongchai Wiwattanachanga,∗, Pham Huy Giaoa

a School of Engineering & Technology, Asian Institute of Technology (AIT)

∗, E-mail: [email protected]

ABSTRACT

This study deals with application of resistivity testing to assess the concrete structures before and after the gigantic flood in 2011 in Pathum

Thani province, one of the most heavily-affected provinces in Thailand. Core concrete samples were taken from concrete structures at five

testing sites and were brought to the laboratory for testing. The results showed that the rebound value, resistivity, and compressive strength

were decreased. The older concrete structures tend to deteriorate more than the younger concrete structures.

KEYWORDS: Resistivity, Compressive Strength, Flood-affected concrete structures

INTRODUCTION

Severe flooding had occurred during the 2011 monsoon

season in Thailand. Commencing at the end of July and trig-

gered by landfall of Tropical Storm Nock-ten, flooding soon

spread throughout the provinces of Northern, Northeastern

and Central Thailand. In October 2011 floodwaters reached

the mouth of the Chao Phraya and inundated parts of the

capital city of Bangkok as shown in Figure 1. Flooding per-

sisted in some areas until mid-January 2012, and resulted in

a total of 815 deaths and 13.6 million people affected. Sixty-

five of Thailand’s 77 provinces were declared flood disaster

zones, and over 20,000 square kilometers of farmland were

damaged. A World Bank’s estimation ranked this disaster as

the world’s fourth costliest disaster as of 2011 surpassed only

by the 2011 earthquake and tsunami in Japan, 1995 Kobe

earthquake, and Hurricane Katrina in 2005, (Zhang, 2011).

The economies of many countries in addition to Thailand

were significantly impacted by the flood, among which the

hardest hit is Japan (McCombs, 2011). Multiple industrial

estates were badly affected by the flood, resulting in manu-

facturing disruptions and global supply shortages as shown in

Figure 2. Thailand’s flood had caused about US$259 billion

in economic losses for the first nine months of 2011. These

losses represented 80% of the world’s total economic losses

and the insurance industry has responded by raising rates in

some areas between 50 and 200 percent or by outright not

accepting new clients in Asia (Cookson & Davies, 2011).

This study is to propose an to assess the health of the

structures affected by the 2011 gigantic flood.

INVESTIGATION OF FLOODED CONCRETE

STUCTURES

An investigation was conducted to accesses the health of con-

crete structures after flooding. Core concrete samples were

taken from five sites in Pathum Thani as shown Figure 3. The

field testing procedure is shown in Figure 4, including several

steps as described in the following. Step 1: is to measure the

elastic properties or strength of concrete following ASTM

C805-97, (ASTM 1997). This test method encompasses the

determination of the rebound number of hardened concrete

using a firmly held spring-driven steel hammer to ensure the

plunger remains perpendicular to the test surface. Step 2

follows ASTM C805-97 (ASTM 1997). The core concrete

samples were taken from five concrete structures in Pathum

Thani province with their ages varying between 10 and 35

years. They have various states of cracking and spalling

resulted from rebar corrosion and environmental impacts

(See Figure 5 a-e). A gasoline-driven core-drilling machine

with a 50-mm diameter diamond bit was used to extract

the core samples. The core positions are shown in Figure

6 from structures of concrete framed buildings of 10 to 35

years in age. Step 3 deals with resistivity measurements

in the lab. Cylindrical samples of 50 mm diameter by 100

mm in length were prepared. The testing setup is shown

in Figure 7. Resistivity measurements were conducted on

the specimens, about 30 minutes after being removed from

the water. Step 4: the porosity test was conducted on three

samples of 50 mm diameter and 100 mm in length. Step 5:

Compressive strength test was conducted to evaluate the in-

situ strength of the concrete. The cylindrical samples were


Flood-affected concrete structures using resistivity measurements

Figure 1: Flood-affected Aareas in Thailand between Octo-

ber and November 2011, (http://www.google.com).

Figure 2: Multiple industrial estates were badly affected by

flooding in Thailand 2011, (McCombs, 2011)

acquired using diamond impregnated drill bits attached to a

core barrel. Preferred length of the capped specimen ranged

between 1.9 and 2.1 times the diameter.

where: ρ (Ωm) is the concrete resistivity; and fc is

compressive strength of concrete.

Figure 9b presented the correlation between concrete

resistivity with effective porosity of concrete structures in

the same condition with Figure 9a. Good correlations were

found as shown in Equation 2a and 2b, with the coefficient

Figure 3: Study Site Locations in Pathum Thani, Thailand.

Figure 4: Field investigation of Concrete Structures.

R2 equal to 0.942 and 0.820 for the condition before and after

flooding, respectively:

ρ = −21.04Φ′c + 417.7, (1a)

ρ = −18.06Φ′c + 384.6, (1b)

where: ρ (Ωm) is the concrete resistivity; and Φe (%) is

the effective porosity of concrete.

RESULT OF FIELD INVESTIGATIONS

Results of measurements on the core samples taken from five

study sites are shown in Table 1-4 and plotted in Figs. 8 and

9. Results shown in Figure 8 a-d indicated that the properties

of the investigated concrete structures vary with the age and

were affected by the flood.

The resistivity of the hardened cement paste varies with

humidity and availability of oxygen, which is affected by the

immersion of concrete.

Figure 9a presented the correlation between resistivity

with compressive strength of concrete structures after three

months of flooding condition. Good correlations were found

as shown in Eqs. 1a and 1b, with the coefficient R2 equal to

0.862 and 0.945 for the condition before and after flooding,

respectively:

ρ = 16.89f ′c − 284.4, (2a)

ρ = 12.79f ′c − 193.7, (2b)


Wiwattanachang and Giao

Site Age Rebound Value, R

Location (year) Above the Flood Level Below the Flood Level

1 10 36 34

2 15 39 36

3 20 33 31

4 30 42 37

5 35 41 36

Table 1: Rebound Value Results in the Flood Zone

Difference in Potential, (mV) Current Intensity, I (mA) Concrete Resistivity, ρ (Ωm)

Site Above Below Above Below Above Below

Location Flood Level Flood Level Flood Level Flood Level Flood Level Flood Level

1 3677 3606 0.48 0.48 145.5 142.7

2 3815 3793 0.46 0.47 157.6 153.3

3 3624 3608 0.55 0.58 125.2 118.2

4 4006 3877 0.40 0.41 190.3 179.7

5 3857 3523 0.44 0.46 166.6 145.5

Table 2: Results of resistivity test.

Max. Load, F (kN) Compressive Strength, f ′c (MPa)

Site Age Above Below Above Below

Location (year) Flood Level Flood Level Flood Level Flood Level

1 10 5201 4993 26.5 25.4

2 15 5436 5164 27.7 26.3

3 20 4867 4632 24.8 23.6

4 30 5770 5240 29.4 26.7

5 35 5632 5069 28.7 25.8

Table 3: Result of compressive strength test.

Bulk Density, g1 (Mg/m3) Apparent Density, g2 (Mg/m3) Effective Porosity, Φc, (%)

Site Age Above Below Above Below Above Below

Location (year) Flood Level Flood Level Flood Level Flood Level Flood Level Flood Level

1 10 2.12 2.28 2.45 2.64 13.2 13.74

2 15 2.27 2.07 2.59 2.38 12.4 12.94

3 20 2.30 2.08 2.67 2.44 13.7 14.73

4 30 2.15 2.11 2.42 2.39 11.1 11.90

5 35 2.39 2.35 2.70 2.69 11.5 12.45

Table 4: Results of resistivity test.



(a) Site 1: Sam Khok

(b) Site 2: Khlong Luang

(c) Site 3: Lam Luk Ka

(d) Site 4: Nong Suea

(e) Site 5: Mueang Pathum Thani

Figure 5: View of study location & column structures

Pathum Thani Thailand

Figure 6: Coring of the flood-affected structure.

CONCLUSIONS

An approach to use resistivity measurements in integration

with other mechanical properties measurements to investi-

gate the health of concrete structures, which were affected

by the 2011 gigantic flood in Thailand was proposed and

successfully conducted. Measurements taken on the concrete

structures above and below the flood level at five testing

sites in Pathum Thani province of Thailand indicated that

the rebound value, resistivity, and compressive strength were

decreased but the effective porosity was increased after the

flood. The older concrete structures tend to deteriorate more

than the younger concrete structures.

REFERENCES

C805, A., 1997. Standard test method for rebound number of

hardened concrete.

Cookson, R. & Davies, P., 2011. Lloyd’s Asian syndicate closes to

new business., Financial Times. http://www.ft.com..

Giao, P., Chung, S., Kim, D., & Tanaka, H., 2003. Electric imaging

and laboratory resistivity testing for geotechnical investigation

of Pusan clay deposits, Journal of Applied Geophysics, 52, 157–

175.

Figure 7: Setup of Resistivity Testing on Concrete Speci-

mens (after Giao et al., 2003).


Wiwattanachang and Giao

(a) (b)

(c) (d)

Figure 8: Comparison of Concrete Structure Properties before and after Flood: a) Rebound Value; b) Concrete Resistivity; c)

Compressive Strength; and d) Effective Porosity

(a) (b)

Figure 9: Correlation between Concrete Resistivity and (a) Compressive Strength as well as (b) Effective Porosity for the

samples of the flooded.



McCombs, D., 2011. Thailand investments put japan inc. directly

in flood’s path, bloomberg, http://www.businessweek.com..

Neville, A., 1998. Properties of concrete, Longman House, Harlow.

Sidney, M., Francis, Y., , & David, D., 2002. Concrete, Prentice

Hall.

Zhang, B., 2011. Top 5 most expensive natural disasters in history.,

http://www.accuweather.com..


Possibility of chemical contamination fromwaste-dumping area to irrigation canal-interpretationbased on geophysical data of an area in Mae Jo, ChiangMai Provinces, Thailand

Noppadol Poomvisesa,∗, Sarawute Chantraprasertb

a Office of Topographical and Geotechnical survey, Royal Irrigation Departmentb Department of Geological Science, Faculty of Science,Chiang Mai Universityc Suwanwajokkasikit Field Corp Research Station, Kasetsart University


ABSTRACT

Geophysical surveys were carried out during an international workshop in a Mae Jo area of Chiang Mai province, as part of the Geoscientists

Without Borders 2011 project, organized by Boise State University and Chiang Mai University. The project was supported by the Society of

Exploration Geophysicists Foundation’s program. The aim of the surveys was to study subsurface structures in the eastern part of the Chiang

Mai basin and to provide the workshop participants with training and work experience on modern geophysical acquisition, processing and

preliminary interpretation. The survey methods include gravity, seismic, magnetic, resistivity and electromagnetic measurements. A survey

line of 2,750 m in length was laid in a southwest-northeast direction. The southwestern part of the line was conducted to pass through an

active waste-dumping area while the middle part was intersected by a main irrigation canal. The northeastern part of the line ran along

the boundary of a large sediment quarry and next to the quarry, the survey line was placed parallel to a sub-irrigation canal. The results of

all geophysical methods correspond to each other and confirm two sets of steeply-dipping normal faults, one fault possibly surfaced near

the canal. The ground water table in this area is rather shallow, approximately 30-40 m deep, with flow directions by gradient towards

the lower level of the irrigation canal. It can be noted that the existence of such subsurface structures associate with shallow ground water

table could result in the area beneath or close to the canal having a high possibility of chemical contamination seeping from the dump area

and the quarry. Importantly, the quarry is still active and it contains a large volume of water at a higher elevation than the irrigation canal.

Also, there is a conceivable tendency of the quarry being adopted as a landfill site of Chiang Mai province in the near future. For these

reasons, a hydrogeology study program should be planned to evaluate the possibility of chemical contamination to the canal. The resultant

information could facilitate in a future program to prevent the possible contamination scenario from occurring.

KEYWORDS: Contamination, geophysical measurement, dump area, landfill, Chiang Mai basin

INTRODUCTION

In January 2011, an international workshop was established

at Chiang Mai province, Thailand. It is as part of the Geo-

scientists Without Borders (GWB) 2011 project, organized

by Boise State University (BSU) and Chiang Mai University

(CMU). Main purpose of this project is to help connect

universities and industry with communities in need using

applied geophysics to benefit people and environmental

around the world. Participants from fifteen institutions from

seven countries, who submitted their application to GWB

homepage, were selected. Conceptually, main method in the

workshop is student-direct training to address social problem

that include engineering and environmental problems and

solutions. Practically, the workshop separated the training

into two main parts, in-field and in-house training.

The in-field training was carried out at two field sites

around Chiang Mai, Mae Jo and Wiang Kum Kam. Mae

Jo is an engineering site while Wiang Kun Kam is a paleo-

archaeology site. The first one will be mentioned in this

paper while the last will be not. Mae Jo site is located

at the eastern part of the Chiang Mai basin (Figure 1). It

is very interesting to survey here as the site of a M5.1

earthquake in 2006. Geophysical surveys were conducted

along rural roads and adjacent farm fields to identify geo-

logic structures and faults related to the seismically active

region. A combination of several geophysical methods can


Chemical contamination based on geophysical data

Figure 1 Location of study area, in red square

Figure 2 Geophysical survey line (in red color)

be helpful as each method has its strengths and limitations,

then the methods used in this study include; gravity, seismic

reflection, resistivity, and electromagnetic. A survey line of

2,750 m in length was laid in a southwest-northeast direction

(Figure 2). The southwestern part of the line was conducted

to pass through an active waste-dumping area while the

middle part was intersected by a main irrigation canal. The

northeastern part of the line ran along the boundary of a

large sediment quarry and next to the quarry, the survey line

was placed parallel to a sub-irrigation canal. This provided

opportunity to the workshop participants with training and

experience on geophysical acquisition during the first week.

Afterwards, all data, field observer’s note and important

information were used as input to the next stage.

The in-house training was taken place in laboratory

at Department of Geology, Faculty of science, CMU. All

participants were separated into six groups based on variety

of observed data and let them participate in any group of

interesting, independently. For a week at the processing

centre, not only that they learned and practiced several

methods of processing and preliminary interpretation, but

also presented the progress of their work to the workshop

once or twice. Subsequently, in January 14th, 2011 they

performed an official presentation at grand seminar room of

Department of Geology and following the presentation was

the closing ceremony. Result of the survey illustrates in a

field camp report of Geophysical imaging of geological and

archaeological targets in the Chiang Mai Basin, A field-based

approach to applied geophysical education. The presentation

and the report at this moment predominantly presents on the

BSU’s website (http://cgiss.boisestate.edu/gwb/index.php/

FieldCamp2010).

Parallel to the work of student, instructors and profes-

sional scientists examined the same geophysical data whether

it can reveal different model of subsurface structure other-

wise it may extend to other valuable scientific researches.

To the author, as a government officer of Royal Irrigation

Department, it can be observed that the main canal is crossing

with the survey line where both the active waste-dumping

and two large quarry with some water infill are located

very close. Under these circumstances, it then raised up

some interesting questions; What is the subsurface structure

underneath the canal? Does it show path of water migration

from the dump site to the canal or not? Does it provide

possibility of chemical contamination from waste dumping

area to the canal or not? According to the doubts, it is

therefore very interested to analyze the existing subsurface

model along the survey line in detail.

By doing so, geology of Chiang Mai Basin was first

briefly reviewed to better understand the regional structure

and further focus to the local structure on the eastern part in

which the study area was located.

SUMMARY GEOLOGY OF CHIANG MAI BASIN

The Chiang Mai Basin is a continental rift basin covering

areas of Chiang Mai and Lamphun provinces (Figure 3). It

forms part of a series of Tertiary basins within a rift zone

that extends southward from northern Thailand to the Gulf of

Thailand. Despite the lack of published subsurface informa-

tion, the basin has been interpreted as having characteristics

of half-graben geometry bounded to the west by an east-

dipping normal fault (Figure 4) (Morley, 2009; Rhodes et

al., 2005). The early movement along the boundary fault was

related to ductile shearing and uplifting of Triassic to Early

Tertiary metamorphic rocks which exposed as high mountain

ranges including Doi Inthanon and Doi Suthep (Macdonald

et al., 2010). The basin fill comprises Oligocene to Pliocene

fluvial and lacustrine sedimentary strata which overlain by

the Present-day fluvial sediment (Morley et al., 2001). The

Tertiary strata grade from poorly sorted matrix-supported

alluvial conglomerate and sandy mudstone near the margins

to lacustrine-deltaic mudstone and sandstone in the basin

center (Figure 5) (Rhodes et al., 2005).


Poomvises and Chantraprasert

Figure 3 Location of the Chiang Mai basin and other Tertiary rift

basins in northern and central Thailand (after Morley et al., 2009)

METHODOLOGY

To carry out the analysis, various information need to be

brought together, such as previous work of ground water

map and profile nearby study area, accompanied with natural

ground elevation above mean sea level, profile of gravity

survey, plan map of magnetic survey, profile of resistivity

dipole-dipole, profile of time-domain electromagnetic sur-

vey, and seismic stacked section of seismic reflection survey.

All information officially derived from GWB field school

and can directly analyze except seismic stacked section.

Although its resolution and clarity were qualified, it must

be re-interpreted since it was the powerful information to

characterize subsurface structure.

The seismic profile across the northeastern part of the

Chiang Mai Basin has been interpreted (Figure 6). Four

seismic horizons were picked including the top Pre-Tertiary

(orange); top syn-rift (green); and two arbitrary horizons in

the upper part of the post-rift strata (yellow and light blue).

The Pre-Tertiary basement is depicted by inconsistent reflec-

tions underlying the divergent reflections with high to mod-

erate amplitudes that were interpreted as the syn-rift strata.

Onlapping the top syn-rift horizon, the reflections in the post-

rift section are sub-parallel and moderately continuous with

low to high amplitudes. East of the canal (triangular blank

area) the four east-dipping normal faults were interpreted

with maximum offset of up to 200 millisecond at the top Pre-

Tertiary level. The offsets decrease upward and most appear

to terminate in the upper part of the post-rift section. The

basin just east of the canal toward the western limit of the

data was cut by a series of west-dipping faults, most of which

appear to continue upward to the surface. One of these faults

has a maximum offset of about 100 millisecond at the top

Pre-Tertiary level and probably reaches the surface around

the location of the canal. A slight drop in elevation of about

2-3 m (Figure 7) was also observed from east to west across

the canal. The east-dipping post-rift reflections, west of the

canal, were interpreted as an eastern limb of an anticline

probably related to an east-dipping inverted normal fault west

of the profile.

RESULT

Interpretation of seismic stacked section shows four dom-

inant strata interpreted as sedimentary sequence deposited

relating to the tectonic event of Chiang Mai rift basin.

The first horizon in orange color is top Pre-Tertiary layer

underlain as basement of the survey line. The second horizon

in green color is lower Tertiaty sedimentary sequence. The

third horizon in yellow color is middle Tertiary. The layer is

rather thick showing several dominant sedimentary structures

inside. Lastly, the light blue horizon is existed in change

between upper Tertiary and Quaternary Terrace. Since

seismic velocity of the layer is 1,542 m/s (Figure 8), it can

also be interpreted as ground water layer underneath the

survey line.

As can be seen in the seismic profile, it can depict

two sets of steeply-dipping normal faults. At the western

part, faults are dipping to the west, while the other half are

generally east-dipping. From the dip direction of layer and

on lapping observed, it exhibits steeply-dipping fault system

and follows by gentle anticline structure. From middle to the

east, faults do not terminate the Top-Tertiary horizon. On the

other hand, from middle to the west, faults mostly terminate

the Top-Tertiary. Although no surface traces of faulting have

been discovered, however, a possible surface fault might

be located near the canal which caused the difference in

elevation between west and east side. In focus, the ground

water table in this area is rather shallow, approximately 25-

30 m as observed in quarry inline and less than 5 m in quarry

beside canal at downstream, with flow directions by gradient

towards the lower level of the irrigation canal.

The comparison of results from different methods may

help to verify the interpretation as the following;

Firstly, comparing with gravity survey (Figure 9), the

method studying change of earth layer in term of density

property, at least there are three anomalies to the survey line

conforming to the stations where faults approached.

Secondly, partial result of Time-domain electromagnetic

survey overlain on seismic section (Figure 10) well supports

that the light blue horizon of seismic survey is remarkable

defined as groundwater table.

Thirdly, interpreted faults on magnetic map shows best



Figure 4 Model for the geometry of the Chiang Mai basin as a half-graben bounded to the west by an east-dipping normal fault (after

Morley et al., 2009).

Figure 5 Model for the sedimentary facies variation in the Chiang Mai basin (after Rhodes et al., 2005)

fit with ones depicted on seismic profile (Figure 11).

Lastly, considering pseudo-depth section of resistivity

dipole-dipole, the method studying layer earth by means of

changes in electrical properties, the resistive sediments to the

west of canal represents Quaternary sediment (Qs), while

conductive sediments to the east of canals corresponds to

terrace deposit (Ts). Moreover, change in conductivity east

of canal may stand for a fault surface. (Figure 12)

Comparison solution, the results of all geophysical meth-

ods correspond well to each other and make enhance high

confidence to the next stage, conclusion and discussion.

CONCLUSION

(i) Along the line of the seismic reflection survey, the data

suggest sets of steeply-dipping normal fault with one set

dipping to the west and the other dipping to the east. In

one possible interpretation, there is, at least, a surface

fault located nearby the canal.

(ii) A part of line between CDP 2100 to CDP 2300 is an

eastern limb of an anticline which probably related to an

east-dipping inverted normal fault, west of the profile.

(iii) Block system initiated between steep-dipping faults

together with ground water table in this area is rather

shallow, approximately 30-40 m deep, with flow direc-

tions by gradient towards the lower level of the irrigation

canal. This migration path provides high possibility of

chemical contamination from waste dumping area to the

canal.

DISCUSSION

This section will discuss some questions that come from

geologist and engineer during preparing this paper and also

points after the conclusion in this paper. The first question

is about the mechanism that will take polluted water from

outside to contaminate into the canal. The answer is chemical

water from dump area seeping down by gravity and along

fault plane, mixing with ground water, flowing along gradient

of limb of anticline to the canal. If ground water table is

nearly the same as the level of water in canal, the impure-

ground water can directly contaminate within the water in

canal. If impure-water table is slightly lower than level of

the water in canal, it can possibly move upward and pollute

to the canal water by capillary force through soil sediment

and along fault plane as well. On the other hand, if water

table is much lower than level of the impure-groundwater,

there is not any possibility of water contamination problem.

In addition, there is surface water to think about. In rainy

season, heavy rain or flash flood can flow from dump site and



Figure 6 Interpretation of the seismic profile across the northeastern part of the Chiang Mai Basin in Mae Jo

Figure 7 Ground elevation along survey line, looking upstream, scale 1 : 1

quarry area directly and take polluted water into the canal

as well. Anyway, in normal circumstances, flowing along

limb of anticline and level of ground water are important

key points making some possibility of water contamination

problem as mentioned.

This leads to an ambiguous point about level of ground

water under the canal. Several geophysical data reveal

different level of ground water but it is uncertain to pin point

which the correct level is. To the authors, geophysical data

were derived by multiple processing steps while processor

possibly added averaging or approximated parameters in pro-

cessing modules which made depth of geological structure,

always different from the level it should be. However, their

shape and pattern of subsurface structure were not much

different. Intuitively, the author observed that level of ground

water near the canal area is shallow, approximately 5-10 m

and 1-2 m higher than water level in canal, as evidenced in

the big pond or big lateritic-quarry, southeastward the cross

point of canal and survey line.

Another quarry at the center of the line, 500 m far from

the cross point, also contains much water inside. Ground

water level is found at about 20 m deep. Difference of

ground surface to the canal is about 25-30 m, it would say

that ground water in the quarry can flow by gradient towards

the lower level of the irrigation canal. Importantly, the quarry

contains a large volume of water at a higher elevation than

the irrigation canal. Also, there is a conceivable tendency

of the quarry being adopted as a landfill site of Chiang Mai

province in the near future. If the quarry is out of control

and chemical-impure water leak to the canal, possibility of



Figure 8 Picking velocity of 1,542 m/s on semblance, interpreted as groundwater table

Figure 9 Interpretation result of gravity method

chemical contamination will shift to environmental impact

or risk, in consequence various problems must be followed

without stay away from.

Next question is about the risk or effect of water con-

tamination. The canal daily conveys much water to irri-

gation area downstream whereas many crops were planted.

Some of them need much water and absorb many nutrient

elements. If contaminated water from canal, which contains

heavy element and toxic chemical substance, is fed to the

crops continuously until much than safety level, agricultural

products will be infected and spread out to the people by

accident.

Somebody informed that irrigation canal fundamentally

constructed follow through the standard safety design which

Figure 10 Reflection seismic versus time domain electromagnetic result. Determine groundwater table at 60 meter below surface.



Figure 11 Figure 11 Comparison of reflection seismic (upper), gravity (middle), and magnetic method (bottom).

should protect water to leak out or move in. It is quite right at

time when it has just finished and/or not more than 20 years

but it did not work after being used every day for 20 years

or more. After long time working, pressure, temperature,

settlement, weathering and erosion could make the canal

crack and become permeable. That is why the issue of

“Possibility of chemical contamination from waste-dumping

area to irrigation canal - interpretation based on geophysical

data of an area in Mae Jo, Chiang Mai Provinces, Thailand”

is expressed to THAICID.

RECOMMENDATION

According to geophysical data interpretation and related

information, lead to the recommendations that should be

performed, in furthers as following;

(i) Investigating several surface faults, especially nearby the



Figure 12 Comparison of reflection seismic and resistivity survey

canal.

(ii) Planning hydrogeology study programs, both short and

long term, to evaluate the possibility of chemical con-

tamination to the canal. The resultant information could

facilitate in a future program to prevent the possible

contamination scenario from occurring.

ACKNOWLEDGEMENT

The issue could not have been achievable without the sup-

port of organizations and individuals. I would like to ac-

knowledge the SEG Foundation’s program and Geoscientist

Without Boarder program for financial contribution. With

respectful, I would like to express my profound to Dr.Lee

M. Liberty, program director for fully support me utilizing

every geophysics data in the program. Over and above,

significant contributions were provided by all in Department

of Geology-CMU, Royal Irrigation Department, Office of

Topographical and Geotechnical survey, Geology Depart-

ment, and Mr.Supawit Yawsangratt for editing and proving

my document. Most of all, my heartfelt thanks are expressed

to all participants in GWB program for their helps during a

good time in field school.

REFERENCES

Liberty, L., Wood, S., Wijk, K., Hinz, E., Mikesell, D., Singhara-

jwarapan, S., & Shragge, J., 2011. The establishment of a

geophysics field camp in northern thailand, The Leading Edge,

30(4), 414–420.

Macdonald, A., Barr, S., Miller, B., Reynolds, P., Rhodes, B.,

& Yokart, B., 2010. PUtUt constraints on the development

of the doi inthanon metamorphic core complex domain and

implications for the evolution of the western gneiss belt, northern

thailand, Journal of Asian Earth Sciences, 37, 82–104.

Rhodes, B., Conejo, R., Benchawan, T., Titus, S., & Lawson, R.,

2005. Palaeocurrents and provenance of the mae rim formation,

northern thailand: Implications for tectonic evolution of the

chiang mai basin, Journal of the Geological Society, 162, 51–63.


Application of geophysical methods for characterizing aselected solid waste disposal site in Songkhla province

Thirat Sommaia,b,∗, Kamhaeng Wattanasena,b, Sawasdee Yodkayhuna,b

a Department of Physics, Faculty of Science, Prince of Songkla University, Hat Yai ,90112, THAILANDb Geophysics Research Center, Department of Physics, Faculty of Science, Prince of Songkla University, Hat Yai ,90112,

THAILAND∗, E-mail: [email protected]

ABSTRACT

Songkhla province mainly uses a landfill method for the solid waste disposal. It is thus possible that the subsurface groundwater or/and soil

can be contaminated by the contaminant, if the waste can leak from the landfill site. This work has applied the geophysics methods

to characterize the subsurface structure in a selected solid waste disposal site in the Songkhla province, which has been previously

recommended by GIS study and to characterize the subsurface structure around an active landfill site of Hat Yai municipality. 2D - IP

& resistivity imaging, Vertical Electrical Sounding (VES), Self-potential (SP), and Seismic refraction surveys have been conducted in those

two sites. The subsurface geological barrier can be obtained by 2D - IP & resistivity imaging and seismic refraction data. The image

of low resistivity clay layer can be mapped and it underlines the higher resistivity top soil layer. In the active landfill site of Hat Yai

municipality, the lateral resistivity variation in the clay layer and the position of discontinuous clay layer are possible the leakage channel

of the contaminant that spread further to the surrounding area. Low chargeability data from IP indicate the present of clay layer which

corresponds to the positive SP data and the low resistivity layer. The study results can suggest the appropriate site with according to the

standard criteria for the subsurface geological structure of landfill site and can probably provide the area of contamination in the ground.

Geophysics method therefore shows that it is a promising tool for site selection study of landfill.

KEYWORDS: Geophysical methods, waste disposal site, contamination

INTRODUCTION

Waste is one of the major problems of the world that

affect to environment. There are many methods for solid

waste disposal such as Open Dumps, Sanitary Landfills,

Incineration, and Ocean Dumping. The advantages and

disadvantages of each method are different and the selecting

method for the solid waste disposal is based on the economy,

society, organization, and landscape. For Thailand, the

suitable method of solid waste disposal is the sanitary landfill

(Pollution Control Department, 2009). However, the leakage

waste from the landfill site will greatly affect to environment

if the subsurface structure of the landfill site has a defect of

no natural barrier e.g. clay layer etc.

There are many ground geophysics methods can em-

ploy to map the subsurface geological structure. The inte-

grated interpretation data from various method can reduce

an ambiguous of the subsurface model and it has been

used in conjunction with other methods such as the seismic

method, the induced polarization (IP) method, and/or the

self-potential (SP) method for the environmental investiga-

tion (Aristodemou E. and Thomas-Betts A., 2000). The

resistivity method has been widely used for landfill and waste

disposal investigation (e.g. Mota, R. et al., 2004; Class, A.,

et al., 2008; Ehirim, C.N., et al., 2009; UGWU,S.A., et al.,

2009; Gemail,Kh.S., et al. 2011; Nwankwo et al.,2012).

Songkhla province locates in the southern part of Thai-

land where mainly uses a landfill method for the solid waste

disposal. It is thus possible that the subsurface groundwater

or/and soil can be contaminated by the contaminant, if

the waste can leak from the landfill site. A GIS study

of Songkhla province for selecting the landfill site was

performed by Rottana (2002) and the appropriate area was

recommended. Two sites from Rottana’s reccomment have

been choosen for geophysical study (Figure 1). The first site

locates in Khuan Lang sub district, Hat Yai district where

the landfill in this site has been actived. The second site is

in Ban Na Wat Pho School, Klong Hoi Khong sub district,

Klong Hoi Khong district.

The aims of this study are: (i) to characterize the

subsurface structure in a selected solid waste disposal site


Geophysical methods for characterizing solid waste disposal

Figure 1 Map of the study area.

and (ii) to characterize the subsurface structure around an

active landfill site of Hat Yai municipality.

SITE DESCRIPTION

Geophysical methods were performed in the two sites in

the western part of Songkhla province. At the Khuan Lang

sub district site, lines of geophysical study are set up in

the northern part of the active landfill, which consist of G1,

G2, and G3 profile (Figure 2 (a)). The length of profile

G1, G2, and G3 is 650, 300, and 400 meters, respectively.

The layout plane of profiles is based on the direction of

groundwater flow in the area, where it flow from south to

north (Chalermyanont, T., 2008).

For the Ban Na Wat Pho School site, there are two

geophysical profiles of G4 and G5 (Figure 2 (b)). The length

of the G4 and G5 is 250 and 255 meters, respectively. In this

site, a geological data from borehole H421 located nearby

the geophysical profiles has been used for constraining the

geophysical interpretation. The groundwater is here flowing

from west-south to east-north direction (Wattanathum, A.,

Figure 2 Map show geophysical profile (red lines) of the both site.

(a) The first site is in Khaun Lang sub district, Hat Yai district. (b)

The second site is in Ban Na Wat Pho School.

2006)

GEOPHYSICAL SURVEYS

To achieve following the purposes of this study, the geophys-

ical survey, 2D - IP & resistivity imaging, Vertical Electrical

Sounding (VES), Self-potential (SP), and Seismic refraction

surveys have been conducted on the same geophysical profile

in the two site (Figure 2).

Induced polarization (IP) method

The induced polarization (IP) method bases on the mea-

surement capacitive action of subsurface which it measures

voltage decay when the transmitted current is turned off. The

chargeability is calculated from the area under the decay

curve (Lowrie,W., 2007). This method has been applied

in conjunction with DC resistivity for a landfill study (e.g.

Aristodemou E. & Thomas-Betts A., 2000; Dahlin,T., et al.,

2002)

The Dipole-dipole chargeability on the profiles conducts

measurements by using the Terrameter SAS 1000 with non


Sommai et al.

polarizing electrodes (Cu-CuSO4 electrodes) for the potential

electrodes.

2D Resistivity method

The resistivity is a measurement resistivity of the subsurface.

Dipole-dipole array has been carried out for this study. The

apparent resistivity for dipole-dipole array can be calculated

by:

ρa = πan(n+ 1)(n+ 2)R [Ωm]

where a is electrode spacing, n is the factor that is increased

from 1 to 8. R is resistance which is read from each

measurement (Loke, M.H., 2000).

This work used two values of smallest electrode spacing,

5 and 15 meters. For electrode spacing equal to 5 meters, nvalues vary from 1 to 6. In addition, n value from 1to 8 was

used smallest electrode spacing 15 meters.

Self-potential (SP) method

The self-potential (SP) method is based on measurement

electrical potential due to current flow on the subsurface.

Some self-potentials are related to man-made disturbances

of environment such as waste disposal site, drainage pipe,

and buried electrical cable (Lowrie, W., 2007). The self-

potential electrode measure potential different between the

reference electrode and the moving one. And the non-

polarizing electrodes are used to be SP electrodes.

The SP profiles were performed on every geophysical

profile of the both sites. Two non-polarizing (Cu-CuSO4)

electrodes that were used with the measuring interval the SP

5 m and was measured by using ABEM SAS 300B.

Seismic refraction method

The seismic refraction is method that is based on measure-

ment travel time of wave from source to receiver. The

wave from source will refract at boundary of subsoil. The

velocity of subsurface can be obtained from travel time and

the subsurface geological structure can be constructed.

This survey uses Smartseis for recording travel time by

using 24 geophones and 7 shot per spread. The geophone

spacing is 4 meters and shot point spacing is 48 meters.

RESULT AND DISCUSSION

2D-IP & resistivity imaging data were inverted by using

RES2DINV program version 3.54 (Loke, M.H., 2000). All

inverted results of IP & resistivity survey used Least-squares

inversion. The inversion model of IP & resistivity were saved

in SURFER format for doing more sophisticated contouring

before interpretation by the SURFER program (Golden soft-

ware, Inc). For the first site, inverted resistivity results divide

resistivity variation to be two zones clearly, which is high

resistivity zone that is in the top soil. The lower resistivity is

in the lower layer and permeates to top soil at some location.

And inverted IP results show very low chargeability zone

(lower 100 millisecond), which it should be represented by

a layer with clay content.

Seismic refraction data was processed by using SIP

program. There are three main steps for processing; the

first step is picking the first break. The second step is

preparing file for using interpretation and the last step is

velocity analysis and creating depth model. All model results

of seismic refraction data show two subsurface structures,

which there is low velocity in the top soil and high velocity

in the lower layer.

The SP result was interpreted after doing drift correction.

SP result is plotted between SP value and measurement

positions. From all SP results show the positive and negative

SP value, which negative SP value is possibly t due to root

activity of tree in the both site.

Example of result

The G2 profile was performed in the rubber tree plantation,

which is in the north of landfill (Figure 2(a)). All inversion

models results of each method is interpreted together. The

SP result has mostly positive SP value and some location

is negative value due to root activity of rubber tree (Figure

3(a)). The inverted resistivity model (Figure3 (b)) has high

resistivity (between 316 and 2512 Ωm) on the top soil and

low resistivity (lower 100 Ωm) in the lower layer. Addition,

some location has low resistivity to permeate on the top soil.

Inverted IP model has mostly very low chargeability (lower

100 msec) (Figure 3 (c)). And the last is seismic refraction

model that show two layers of subsoil structure (Figure 3

(d)). The velocity of the top soil is an average of 468 m/s

with 3 meters thickness. And velocity of the second layer

is an average 1895 m/s, which should be represented by

clay layer. The geological data of H853, which is ≈ 2km

from G2 profile, shows alternate between clay and thin sand

(≈ 3meters thickness) layer. The first layer of borehole is

12 meters clay thickness. It’s associated with all inverted

models.

The inversion of 2D-IP & resistivity models found low

resistivity (less than 32 Ωm) anomaly. In the center of this

zone, the resistivity is very low ( ≈ 3 Ωm) and chargeability

ranges between 80 and 90 msec. At this point, there should

have groundwater permeate in clay layer. The previous

research about groundwater flowing direction shows that

groundwater flow northward. Thus, it is possible that the

groundwater may transport the waste from the landfill to

contaminate in the environment.

CONCLUSION

The integrated interpretation geophysical methods has been

performed and the lithologic data from borehole in the vicin-

ity of study area has been used to constrain the interpretation

borehole data indicates the structure of subsurface in the


Geophysical methods for characterizing solid waste disposal

Figure 3 A geophysical results of G2 profile.(a) the SP result(rubber tree is represented with green point), (b) the inverted resistivity model

, (c) the inverted induced polarization (IP) model, and (d) the seismic refraction model.

northern part of the active Hat Yai landfill is mostly clay

layer, which is good natural barrier and agree well with

geophysical methods. Geophysical method can applied to

motoring for the environmental problem. The combined

geophysical methods make interpretation greatly. Resistivity

measurement is suitable method for monitoring contamina-

tion and/or environmental problem. Using IP measurement

combine with resistivity method clearly make interpretation

of clay content layer. SP method is not good method for

this study area due to amount of root activity of the trees.


Sommai et al.

And seismic refraction method provides a very good shallow

subsurface structure that the natural barrier (clay) layer can

be clearly mapped.

ACKNOWLEDGMENT

This work is supported by research grants from the Gradu-

ate studies and department of Physics, Faculty of Science.

Furthermore, we wish to thank my friends for helping in

geophysical field work.

REFERENCES

Aristodemou, E. & Thomas-Betts, A., 2000. DC resistivity and

induced polarization investigations at a waste disposal site and

its environments, Journal of Applied Geophysics, 44, 275–302.

Arora, T. et al., 2007. Non-intrusive characterization of the redox

potential of landfill leachate plumes from self- potential data,

Journal of Applied Geophysics, 92, 274–292.

Chalermyanont, T. et al., 2008. Aquifer characteristic and quality

of groundwater in the vicinity area of the Songkhla lake, Hat Yai

basin, Tech. rep., Prince of Songkla University.

Chandra, S. et al., 2010. Geophysical model of geological dis-

continuities in a granite aquifer:analyzing small scale variability

of electrical resistivity for groundwater occurrences, Journal of

Applied Geophysics, 71, 137–148.

Class, A. et al., 2008. Assessing aquifer vulnerability to pollutants

by electrical resistivity tomography (ERT) at a nitrate vulnerable

zone in NE spain, Journal of Environmental Geology, 54, 515–

520.

C.N., N. et al., 2012. Geophysical method of investigating

groundwater and sub-soil contamination-A case study, American

Journal of Environmental Engineering, 2(3), 49–53.

Dahlin, T., Leroux, V., & Nissen, J., 2002. Measuring techniques

in induced polarization imaging, Journal of applied geophysics,

50, 279–298.

Department, P. C., 2009. Sanitary landfill, Tech. rep., Pollution

Control Department.

Edet, A. et al., 2002. Delineation of shallow groundwater aquifers

in the coastal plain sands of Calabar area using surface resistivity

and hydrogeological data, Journal of Applied Geophysics, 35,

433–443.

Ehlrim, C. & Ofor, W., 2011. Assessing aquifer vulnerability

to contaminants near solid waste landfill sites in a coastal

environment,Port Harcourt, Nigeria, Trends in Applied Sciences

Research, 6, 165–173.

Frohlich, R. K. et al., 2008. Investigating changes of electrical char-

acteristics of the saturated zone affected by hazardous organic

waste, Journal of Applied Geophysics, 64, 25–36.

Gemail, K. S. et al., 2011. Assessment of aquifer vulnerability

to industrial waste water using resistivity measurements. A case

study, along El-Gharbyia main drain, Nile Delta, Egypt, Journal

of Applied Geophysics, 75, 140–150.

Genelle, F. et al., 2012. Monitoring landfill cover by electrical

resistivity tomography on an experimental site, Journal of Engi-

neering Geology, 145-146, 18–29.

Georgaki, I. et al., 2008. Evaluating the use of electrical resistivity

imaging technique for improving CH4 and CO2 emission rateestimations in landfills, Journal of Science of the Total Environ-

ment, 389, 522–531.

Ladachart, R., 2002. Area University selection for solid waste

disposal at changwat Songkhla, Master’s thesis, Faculty of

Science, Chulalongkorn.

Loke, M., 2000. Electrical imaging surveys for environmental and

engineering studies.

Lowrie, W., 2007. Fundamentals of geophysics, Cambridge

University Press, 2nd edn.

Meesin, W., 1996. The contamination of some pollutants in

groundwater, Amphoe Hat Yai, Changwat Songkhla, Master’s

thesis, Prince of Songkhla University.

Milsom, J., 2003. Field Geophysics, Chichester, 3rd edn.

Mota, R. et al., 2004. Granite fracturing and incipient pollution

beneath a recent landfill facility as detected by geoelectrical

surveys, Journal of Applied Geophysics, 57, 11–22.

Parasnis, D., 1997. Principles of Applied Geophysics, Methuen

Co.Ltd, 5th edn.

Robinson, E. S., 1988. Basic Exploration Geophysics, Canada.

Schmutz, M. et al., 2006. Azimuthal resistivity soundings over a

steeply dipping anisotropic formation: A case history in central

Tunisia, Journal of Applied Geophysics, 60, 213–224.

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tivity& IP inversion using the least-squares method., Geotomo

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Press, 2nd edn.

Thongnark, K., 1998. Solid waste management of municipalities

and sanitary districts in Southern Thailand, Master’s thesis,

Prince of Songkla University.

Ugwu, S. et al., 2009. Effect of waste dumps on groundwater in

Choba using geophysical method, Journal of Applied Sciences

Environmental Manage, 13(1), 85–89.

Wattanathum, A., 2006. Numerical Simulation of Groundwater

Flow in Hat Yai Basin, Changwat Songkhla, Master’s thesis,

Prince of Songkhla University.


Detection Leakage Reservoir located on Fault zone andKarst Topography by Dipole-Dipole Resistivity andSeismic refraction survey : A case study at Ban PhraJaedee Sam Ong reservoir, Karnjanaburi ProvinceThailand

Tirawut Na Lampanga,∗, Anchalee Kongsuka, Benjamas Sawatdiponga, Noppadol Poomvisesa,

Narucha Sangtonga

a Geophysics group, Geology section, Office of Topographical and Geotechnical survey Royal Irrigation Department, Dusit,

Bangkok, Thailand∗, E-mail: [email protected]

ABSTRACT

Three Pagoda Fault zone in Kanchanaburi province, Thailand was intuitively generated by plate tectonic activities and karst topography

uplift in Ordovician and Permian period and possibly causing many critical conditions covering the western Thailand region. A case study

of irrigation reservoir at Ban Phra Jaedee shows the influence of the fault zone induced the leakage of reservoir. The still-active fault may

be initiated fractured zone, evolution of karst topography, cracking along contact of rock unit, and abnormal structure of reservoir

foundation that led to recession of water level in reservoir.

Geological mapping and geophysical investigations by resistivity dipole-dipole and seismic refractions method have been used for detect

leakage zone in reservoir area. Alignment in survey line was designed based on geological investigation data from reservoir basin. There

were five survey lines in this account, one along the center line of dam crest, while the other were irregular gridding covering the reservoir

area. Resistivity imaging and seismic refraction measurement were therefore applied along those lines mentioned.

Result from dipole-dipole resistivity complied with refraction seismic measurements indicates that the leakage was taken place on

foundation of reservoir, through the existing sinkhole, and especially at karst-contact zone which were controlled by the Three Pagoda

Fault system nearby. In consequent, the result of geological investigation, as important information, was later used as a guideline to the

drilling plan for cross checking, before reservoir treatment program in the next stage.

INTRODUCTION

Ban Pra Jaedee Sam Ong Reservoir is located at Kan-

chanaburi province in western part of Thailand and close

to Three Pagoda Fault zone (Figure 1) at co-ordination of

436838E/1692372N. Type of dam is rock fill (Figure 2), with

approximately 820 meters in length, and 11 meters in height.

Reservoir was leakaged in only 2-3 months after rainy season

every year. There was some previous assumptions that the

abnormal structure of foundation may be the cause of the

recession of water. For this reason, geology and geophysical

investigation were then applied to detection anomalous zone

and analyzed the cause of leakage in this reservoir.

GEOLOGY AND FIELD STUDY

By and large, reservoir area consists of limestone and clastic

sedimentary rocks of Ordovician to Permian age. Evidence

from limestone exposures shows many traces of slicken side

Figure 1 Ban Phra Jae Dee reservoir and Three pagodas fault at

west side.


Leakage detection by dipole-dipole resistivity and seismic refraction

Figure 2 Illustrated rock fill dam.

Figure 3 Trace of slicken side on limestone exposure.

(Figure 3), and fault gouge material (Figure4) yielding the

solution of controlled geological structure in reservoir area.

It is two sets of fault with orientation of N59E, 34S and

S5E, 33S accompanied with the folding structure (Figure

5) with axial plane of S25E, 21N and axis of 40/190.

Faulting and folding in the area were generated from Three

Pagoda Fault system, western side of the area and still be

Figure 4 Illustrated fault gouge material.

Figure 5 Illustrated folding.

Figure 6 Illustrated a sink hole with approximately 4 meters in

diameter, and 0.3 meters in depth.

active until the present day. An existing sink hole founded

at the floor of reservoir is also an evidence of the active fault

activity (Figure 6).

METHODOLOGY

Geophysical measurements of dipole-dipole resistivity and

refraction seismic method were applied to study the leakage

problem. Information obtained from field survey was prelim-

inary used to design the alignment of gridding pattern of five

survey lines described as follows (Figure 7);

(i) Line survey A−A′ along center line of dam, 820 meters

in length.

(ii) Line B − B′ along left rim of reservoir, 605 meters in

length.

(iii) Line C − C ′ along floor of reservoir, 385 meters in

length and parallel to line B −B′.

(iv) Line E − E′ along right rim of reservoir, 550 meters

length.

Both dipole-dipole resistivity and refraction seismic

were carried on the same line, place, and position for cross

checking the result to each others.


Na Lampang et al.

Figure 7 Geophysical survey lines.

Figure 8 Resistivity dipole-dipole compile with seismic refraction along line A−A′.

In processing stage, resistivity dipole-dipole was pro-

cessed by RES2DINV software version 3.4, developed by

Geotomo software, 2001, Malaysia, and subsequently pre-

sented in 2-D pseudo resistivity profile.

Seismic refraction was processed using reciprocal time

method previously by Hagiwara’s graphic method and af-

terwards by SeisRefa application software, USA., and lastly

performing the 2-D depth profile.


After comparison and combination of dipole-dipole resistiv-

ity and seismic refraction result, the well matching between

low resistivity zone related with low velocity zone of seismic

refraction exhibit several anomalous parts in the five survey



Figure 9 Resistivity dipole-dipole compiled with seismic refraction along line B −B′.

Figure 10 Resistivity dipole-dipole compiled with seismic refraction along line C − C′.

line which were interpreted as the area of leakage as follows;

(i) Line A−A′, leakage 7 zones (Figure 8)

(ii) Line B −B′, leakage 4 zones (Figure 9)

(iii) Line C − C ′, leakage 2 zones (Figure 10)

(iv) Line D −D′, leakage 1 zone (Figure 11)

(v) Line E − E′, leakage 1 zone (Figure 12)

Nevertheless, there are some anomalies as low velocity

zone of seismic refraction survey showing irrelevant that of

resistivity result, such as line D −D′ and line E − E′. It is

possible that specific composition and properties of founda-

tion rock in both lines are the cause of the unconformable

case mentioned. It can explain that apart of limestone


Na Lampang et al.

Figure 11 Resistivity dipole-dipole compiled with seismic refraction along line D −D′.

Figure 12 Resistivity dipole-dipole compiled with seismic refraction along line E − E′.

foundation has fractured zone inside but has not water or

moisture filled in the fractured space therefore it generates

low velocity but not generates the low resistivity anomaly.

CONCLUSION

At Ban Prajaedee Sam Ong reservoir, it can separate leakage

of water into 3 models as follows;

(i) Water leaked from reservoir body through some part of

compacted soil and along boundary of rock foundation.

(ii) Water leaked through sinkholes around reservoir area

especially in line E − E′.

(iii) Water leaked through the contact of rock unit along the

old river.



ACKNOWLEDGMENTS

The author would like to acknowledge to Mr.Narucha

Sangthong, director of geology section, for his advice in

seismic refraction interpretation, Miss Anchalee Kongsuk,

senior geologist, for her suggestion in resistivity dipole-

dipole interpretation, and also Mr.Noppadol Phumvieses,

senior professional geologist, for discussion in several view-

points.

Most of all, special thank extends to all member in

Geophysics group, Geology section, Office of Topographical

and Geotechnical survey, Royal Irrigation Department, for

their helping in acquisition work, and providing geological

information data from feasibility geology report. Lastly

from my heart, I would like to thank to whom may not be

mentioned their name for their kindly cooperation in kinds

of work.

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of north american geology.

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Methods in Exploration and Engineering, Univercity of New

South Wales.

Matsubara, Yoshikazu, Kudo, Hiroshi, Nakano, Takuji, Takeuchi,

& Toshiaki, 1988. Lecture notes for advance course on seismic

surveys for geotecnical.

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poration, International Institute of Seismology and Earthquake

Engineering, Building Research Institute.

OYO corporation, 1992. Course note on seismic prospecting.

Royal Thai Survey Department, 2002. Topographic map, scale

1:50,000 sheet 4639 i series l 7018 (ban prajaedee sam ong).

Sangthong, N., 1996. Manual interpretation compile with seisrefa

of seismic refraction correlation with logging data from drilled

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Cambridge University Press, Cambridge.

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sam ong project, sungkraburi, kanchanaburi memo g. 30/2532„

Geology section, Royal irrigation Department.

Telford, W., art, G., & Lapland Sheriff, R., 1990. Applied

Geophysics, Cambridge University Press, Cambridge.


Fault Delineation Using Magnetic Data in the Eastern Partof Chiang Mai Basin

Chawanun Ninsoma,∗, Siriporn Chaisrib,c, Sarawute Chantrapraserta

a Department of Geological Sciences, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailandb Department of Physics and Materials Sciences, Faculty of Science, Chiang Mai universityc ThEP, Commission of Higher Education, 328 Si Ayuthaya Road, Bangkok 10400


ABSTRACT

Fault is displacement of rocks along a shear surface and its location can be represented as a variation in a magnetic field. Fault location can

provide information on earthquake hazard estimation and hydrothermal sources and related geological resources. The study area is located

in the eastern part of the Chiang Mai basin that is covered by Quaternary and Tertiary sediment. The Euler deconvolution is a technique

for estimating the depth and the location of magnetic sources, based on the solution of Euler’s homogeneity equation. Euler deconvolution

has increasingly been used as an aid in interpreting profile or gridded magnetic data. In applying Euler deconvolution, one must select an

appropriate structure index that is a measure of the rate of change of the field with distance. Windows of varying sizes were employed to

limit the number of grid calculation in the equation. A trial and error technique was used to determine the best number of structure index

and window size. Prior to applying Euler deconvolution to magnetic data, earth main magnetic field correction and Reduction to the Pole

(RTP) filter were applied for data symmetry in the low-latitude area. From the Euler interpretation map of aeromagnetic data, some N-S

trending faults can be detected with depth of about 500 - 2,000 meters. The aeromagnetic data cannot detect shallow faults because of

the limit of flight line interval resolution. Therefore ground magnetic survey has been conducted. From ground magnetic data, the Euler

interpretation map shows a near surface fault with depth of about 60 - 500 meters and a NE-SW trend. This particular fault has previously

not been documented and located near the city area. The results from this study provide vital information for earthquake hazard mitigation

and city planning.

KEYWORDS: Magnetic survey, Euler deconvolution, Position and depth estimation, Fault

INTRODUCTION

There has been a number of earthquake events with a mag-

nitude range of 1-3. An event with 5.1 magnitude scale

was recorded at Mae Jo, Sansai District in 2006. These

earthquakes have been associated with an uninterpreted fault.

Current city development requires accurate mapping of the

subsurface geology and locating potential earthquake haz-

ards, especially those associated with active faults.

Magnetic survey is a potential field technique which

measures existing magnetic field strength of the Earth’s crust.

It is useful in investigating subsurface geology, archaeology

and mineral exploration because it is cheaper and faster than

other geophysical surveys. In this study, the investigation of

subsurface structural geology will be carried out using the

Euler deconvolution of magnetic data to estimate the depths

and position of magnetic anomalies.

Magnetic anomaly usually associates with igneous base-

ment due to the higher possibility of magnetic mineral

contents that of sedimentary basin fills. Fault is displacement

of rocks along a shear surface and the fault anomaly can be

represented as a variation in a magnetic field. Magnetic pro-

file across a fault is zero with maximal and minimal values

on the flanks. Fault location can provide the information on

earthquake hazards, and hydro-thermal sources. Fault related

structures also have implication for petroleum and mineral

prospecting.

GEOLOGICAL SETTING

The Chiang Mai Basin is one of the largest continental rift

basins in northern Thailand (Polachan and Sattayarak, 1989).

It covers an area of about 3000 km2, with a maximum width

of about 35 km and a N-S length of about 140 km.

The mountainous areas surrounding the basin are com-

posed of Precambrian gneiss and calc-silicate rocks overlain

by Palaeozoic sedimentary and volcanic rocks intruded by

Triassic granite (Baum et al., 1970; Piyasin, 1972; Suen-


Fault delineation using magnetic data

Figure 1 Location map of study area, Chiang Mai basin from

Google Earth software, with earthquake events in circles, possibly

unknown faults in blue and green lines, Mae Tha fault a red line,

and ground magnetic survey area outlined by red dashed line.

silpong et al., 1977)

The study area is located in the eastern part of the Chiang

Mai Basin that is covered by Quaternary to Tertiary gravel,

sand, silt, clay and laterite. The underlying basement is

mostly Carboniferous sedimentary rocks. These rocks were

compressed into N-S trending folds and reverse faults that

were cross-cut by N-S trending normal faults and NE-SW,

NW-SE trending strike-slip faults. The Mae Tha Fault is

one of the important faults east of the Chiang Mai Basin

and has been mapped as an active fault by the Department of

Mineral Resource (DMR, 2011). Ban Thi District is located

in the northern part of the Lamphun Province where there

has been frequent earthquake events. Ban Thi is covered

by Quaternary alluvial sediment. In satellite images, the

sediment cover in the study area corresponds to two different

shades divided by the Kuang River, where an unknown fault

is possibly located.

Based on information from satellite images, GIS data,

groundwater, earthquake locations, and total-count radioac-

tivity map, Chantraprasert (pers. comm.) interpreted un-

known faults (blue and green lines in Figure 1) in the vicinity

of the Chiang Mai and Lamphun city areas. These faults were

not previously mapped by Department of Mineral Resource

(DMR).

MAGNETIC DATA

The aeromagnetic data in northern Thailand was obtained

from the Department of Mineral Resources with gridded

spacing of about 100 meters at 1,000 feet elevation covering

the Chiang Mai Basin area. Ground magnetic surveys in

the Ban Thi area, Lamphun Province, have been conducted

where the prospective fault is located. The survey cover an

area of about 16 km2 using cesium magnetometer reading at

0.5 second interval and were recorded using a base station for

diurnal correction. The geomagnetic field parameters for the

study area are 0.46 Declination and 25.27 Inclination from

National Oceanic and Atmopheric Administration (NOAA).

The Earth main magnetic field removal was applied to both

aeromagnetic and ground magnetic data as shown in Figures

2 and 3, respectively.

DATA PROCESSING

Both ground and air-borne magnetic data contain some high-

frequency noise such as the effect from near surface struc-

tures and that from high-voltage power lines with 50 to 150

meter electromagnetic effects (Mizoue et al., 2004). Also,

the edges of magnetic anomalies are not resolved because

in the low-latitude areas (the magnetic field inclination in

this area is 25), the magnetic signals are dipolar over

causative bodies (Figures 2 and 3). Processes such as upward

continuation, reduction to the pole, Euler deconvolution were

implemented in the gridded magnetic data.

Upward continuation

The magnetic data contain various minor anomalies that are

not related to regional structures. An upward continuation

method was calculated and applied to eliminate or minimize

such noise and the effects of shallow sources (Henderson &

Zietz, 1949).

Reduction to the pole

Reduction to the pole (RTP) is the method that transform

dipolar magnetic to monopolar anomalies (inclination = 90°)

for data symmetry in the low-latitude area and can simplify

the interpretation of the data (Al-Garni, 2010).

After upward continuation and RTP were applied to

aeromagnetic and ground magnetic gridded data , the output

maps have smoother and symmetrical magnetic anomalies

(Figures 4 and 5).

After 1,000m upward continuation and reduction to

the pole, the aeromagnetic data show the location of the

causative bodies from residual magnetic anomaly (Figure 4)

and NW-SE trending magnetic lineations in the northern part

of the basin and NE-SW trending lineations in the south.

High magnetic anomalies west of the basin are probably

related to granitic rocks.

After 200 m upward continuation and reduction to the

pole, ground magnetic data have high magnetic anomalies in

the central area and low anomalies in the eastern and western

parts of the study area (Figure 5). Magnetic lineations trend

N-S and NE-SW.

Euler deconvolution

Euler deconvolution has been widely used as an aid for

interpreting profile or gridded magnetic data to provide


Ninsom et al.

Figure 2 Magnetic anomalies from aeromagnetic survey over the

Chiang Mai basin after Earth main magnetic field removal.

Figure 3 Magnetic anomalies from ground survey overing the Ban

Thi area, Lamphun province after diurnal correction and Earth main

magnetic field removal.

estimates of geometrical parameters. This method assumes

that the anomaly is in the homogeneous function of spatial

Figure 4 Magnetic anomalies from aeromagnetic survey after

application of 1,000m upward continuation and RTP filters. H

is location of high magnetic anomalies and L are location of low

magnetic anomalies.

coordinates. This method was first reported by Thomson

(1982) and Reid et al. (1990) in order to detect the depth

of causative bodies. The theory is based on the Euler’s

homogeneity equation which relates the potential field and

its gradient components to the location of the source with the

degree of homogeneity (Blakely, 1982).

Euler’s equation could usefully be written in the form

(Thompson, 1982)

(x−x0)∂T

∂x+(y−y0)

∂T

∂y+(z−z0)

∂T

∂z= −N(T−B) (1)

Where (x0, y0, z0) is the position of a magnetic source

whose total field T is measured at location (x, y, z), and

the total field has a regional value of B. The degree of

homogeneity is N .

Equation (1) can be solved exactly for the unknowns

(x0, y0, z0) and B by establishing the structural index N and

evaluating the derivatives and total field values at four or

more points within the x-y window. This results in more than

four linear equations in four unknowns. The window size

is a function of the grid cell size and should cover an entire

anomaly, but it should not include anomalies from more than

one object.

Vertical derivative filter,∂T∂z

, computes the vertical rate

of change in magnetic field. Horizontal derivative filters,



Figure 5 Magnetic anomalies from ground magnetic survey after

application of 200m upward continuation and RTP filters. H are

location of high magnetic anomalies and L are location of low

magnetic anomalies.

∂T∂x

and ∂T∂y

, compute the x-direction and y-direction rate

of change in magnetic field. Both vertical and horizontal

derivative are applied in Euler deconvolution solution in

gridded data.

In applying Euler deconvolution method one must select

an appropriate structure index and window size as algorithm

parameters. The window size should be large enough to

incorporate sufficient variation of the field and its gradients,

and small enough to minimize computation time and avoid

effects of neighboring anomalies. (Kuttikul P, 1995).

In applying the technique to magnetic data, the proce-

dure can be repeated several times using different window

sizes and structure indices, (Table 1.), to obtain the best

solutions.

A given point in a gridded data set, a set of simultaneous

equation within a limited window is solved. Magnetic data,

horizontal and vertical derivatives at each grid point in the

window are used to solve Euler’s equation. The uncertainty

or standard deviations of the local and depth solutions are

also obtained, and these can be used as criteria to accept or

reject a solution.

For aeromagnetic data, the Euler deconvolution was

applied to the map in Figure 4 with the best window as

20×20 and 0.0 structure index and the Euler’s solution is

shown in Figure 6. For ground magnetic data, that was

applied to the map in Figure 5 with 0.0 structure index and

Figure 6 Euler deconvolution map of aero-magnetic data of Chiang

Mai basin with 20×20 window size and 0.0 structure index. The

colour circles show depth variation of Euler point and interpreted

lines of Euler point depths at 200 - 2,000 meters.

Figure 7 Euler deconvolution map of ground magnetic data of the

Chiang Mai Basin with a 15×15 window size and 0.0 structure

index showing linear patterns of Euler point depths with fault

interpretation lines.

15×15 for window size solution and the Euler’s solution is

shown in Figure 7.


Ninsom et al.

Figure 8 (a) interpretation of aeromagnetic data of the Chiang Mai

Basin and (b) interpretation of ground magnetic data in the southern

part of the basin.

Table 1 Structural index, N, for magnetic interpretation using Euler

deconvolution. (Thompson, 1982; Reid et al., 2003; 1990)

Geologic model Magnetic SI

Contact 0.0

Thick step (Fault) 0.5

Sill/Dyke 1.0

Pipe 2.0

Sphere(point source) 3.0

RESULTS AND DISCUSSION

The Euler deconvolution result map of aeromagnetic data

(Figure 6) has clustering of Euler depth points along NW-SE

trends in the northern part of study area and NE-SW trends

probably evidence for conjugate fault sets.

The Euler depth points of the structure index 0.0 (con-

tact) at about 300 - 2,000 meters depth between high linear

magnetic variation that does not correlate to any near-surface

fault. However, ground magnetic data with Euler deconvo-

lution has near-surface Euler depth points 20 - 200 meters

deep.

Both aeromagnetic and ground magnetic surveys with

application of different algorithms give a clear picture of

the subsurface structures that might be faults. Therefore,

the results from this study provide vital information for

earthquake hazard mitigation and city planning. However,

this study will be reliable if combined with other geophysical

methods.

CONCLUSIONS

The Euler deconvolution of aeromagnetic data (Figure 8)

delineates a fault trace that follow the position of a previously

interpreted fault (blue line in Figure 1). However, the same

data do not indicate any fault along the green fault line.

Ground magnetic survey could be carried out for better

resolved magnetic data.

Euler deconvolution of ground magnetic data (Figure 8b)

has small linear features parallel to a previously interpreted

fault.(blue line in Figure 1) These could probably correspond

to either faults or other lithological contacts.

ACKNOWLEDGMENTS

The authors’ appreciation is extended to reviewers for their

insightful comments and suggestions on this manuscript.

Preliminary field work was funded by the Graduate School,

Faculty of Science, Chiang Mai University and ThEP, Com-

mission of Higher Education. The Department of Mineral

Resources provided access to aeromagnetic data and relevant

processing software.



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of anomalies in total magnetic intensity fields, Geophysics, 14,

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& Nitta, H., 2004. Residential proximity to high-voltage

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computer-assisted depth estimates from magnetic data, Geo-

physics, 47(1), 31–37.


Geophysical Surveys to Detect Potential Active Faults inSan Sai District, Chiang Mai Province

Tanapon Suklima, Suwimon Udphuaya,b,∗, Siriporn Chaisria,b, Sarawute Chantrapraserta

a Department of Geological Sciences, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailandb ThEP, Commission of Higher Education, 328 Si Ayutthaya Road, Bangkok 10400, Thailandc Department of Physics and Materials Sciences, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand


ABSTRACT

The earthquake with a magnitude of 5.1 Mw occurred in San Sai District, Chiang Mai Province on 13 December 2006 was considered

an uncommon event. This is because there was no statistical record of such significant earthquakes in the area, although several minor

earthquake events have been documented by the Department of Mineral Resources and the United States Geological Survey catalogues.

Therefore, the earthquake might have been associated with a potential active fault zone within the Chiang Mai Basin. San Sai District

is located in the eastern part of the Chiang Mai Basin. Exposed in the area are mostly fluvial Quaternary and Tertiary sediment and

sedimentary rocks. The underlying basement exposed at the nearest basin margin includes Carboniferous to Permian clastic and carbonate

sedimentary rocks and Permian volcanic rocks. The objective of this study is to investigate the existence of potential active faults that might

have been related to the earthquake in San Sai District. An integrated geophysical investigation, including gravity, 2D electrical resistivity

and seismic reflection techniques, was carried out along two profiles across the prospective north-south trending fault interpret, previously

interpreted based on satellite images and low-resolution air-borne geophysical data. Although structural imaging using geophysical surveys

in poorly stratified semi-consolidated sediments underlain by a strongly deformed sedimentary and volcanic basement is very challenging,

the reflection seismic and 2D electrical resistivity data reveal two sets of normal faults: the first cutting the lower part of the section and

the second the upper part. A few faults have offsets across an inferred water table and appear to continue upward to the surface. These

particular faults are appropriate candidates for being the active faults related to recent earthquake activities in the area. This potential active

fault system should be studied in more details to confirm its geometry, orientation and lateral extent.

KEYWORDS: Resistivity, Compressive Strength, Flood-affected concrete structures

INTRODUCTION

The earthquake magnitude of 5.1 occurred in the San Sai

District area on December, 13, 2006 was considered uncom-

mon. This is due to the fact that there was no statistical

record of such significant earthquake in the area although

several earthquake events have been recorded (epicenter

locations shown in Figure 1, according to the Department of

Mineral Resources (DMR), Thailand and the U.S. Geological

Survey (USGS) catalogues, DMR, 2011 and Petersen et al.,

2007). The epicenter of the 2006 San Sai earthquake was

located at roughly 18.93N, 99.00E which was not on the

Mae Tha fault zone. Therefore the earthquake might have

been associated with a potential active fault zone within the

Chiang Mai Basin. Interpretation of subtle river and stream

patterns on satellite images indicated a possible fault trending

approximately north-south from San Sai and Mueang Chiang

Mai Districts of Chiang Mai Province to Mueang Lamphun

District of Lamphun Province (Figure 1) However, there is

insufficient geological evidence at the surface to explain the

existence of such fault because it is probably covered by

the recent fluvial sediment during a long quiescence period.

Therefore, this paper presents results from an integrated

geophysical investigation of the potentially active Fault in

San Sai district where located 20 km northeast of Mueang

Chiang Mai district (Figure. 2). The main aim of the

investigation was used geophysical data to image this subsur-

face fault in order to understand its geometry, location, and

orientation. The results from various geophysical surveys

conducted in this report including resistivity, gravity and

seismic methods. The geophysical survey will be very useful

for future preparation of possible earthquake hazard that may

be related to the movement of this potential active fault.


Geophysical surveys to detect potential active faults

Figure 1 Location of the possible unknown faults shown in blue

and green lines, and the field survey area in red dash rectangle. Red

line is Mae Tha Fault.

GEOLOGICAL SETITNG

San Sai District area is located in the eastern side of Chiang

Mai Basin, covered by Quaternary and Tertiary sediments

including gravels, sands, silts, clays, and laterites. The

underlying basement is Carboniferous sedimentary bedrock

(Liberty L. M., 2011). Structural geology of areas is mainly

north-south trending extensional faults. The Mae Tha fault

zone appears as a curved line (Figure 1) along the eastern

margin of the Chiang Mai Basin from Doi Saket and San

Kampang Districts to Lamphun Province, is one of the main

faults that located by the DMR.

GEOPHYSICAL SURVEYS

Geologic faults have specific physical characteristics, so they

are susceptible to detection and mapping by geophysical

methods. In this study gravity, resistivity and seismic

methods of geophysical surveys were applied to model the

shallow geological structure, locating and constructing a

2D subsurface geometry for the subsurface faults associated

with the earthquake occurrences in San Sai District, Chiang

Mai Province. The surveys were carried out on east-west

direction of 1km long for San Sai-01 (SS-01) and 1.2 km for

San Sai-02 (SS-02) across the prospecting fault trend (Figure

2).

GRAVITY METHOD

Gravity measurements investigate the subsurface geology by

measuring horizontal variations in the earth ’s gravitational

field generated by density difference between subsurface

rocks (Kearey and Brooks, 1991). The aim of the gravity

surveys for this research was to determine the location of the

fault plane.

The gravity value of each point was measured with a

SCINTREX Autograv gravimeter, model CG-3.Thirty-four

and Thirty-nine measuring points were place along profile

Figure 2 Location map showing survey SS-01 (AA’) and SS-02

(BB’) across the prospecting fault line.

Figure 3 Bouguer anomaly of, a) survey SS-01, b) survey SS-02.

SS-01 and SS-02, respectively. The spacing measuring point

was generally 30 m. The location and elevation of measuring

point was determined with a GPS for base station (reference

point) and used the total station to correct the position and

elevation of another measuring point for both survey lines.

The measured gravity values were corrected for effect of

instrumental drift and tides, latitude, elevation, and free air

with a density is 2.67 kg cm3 (Average earth ’s crust density).

A Bouguer anomaly value was drawn and used for interpre-

tation in order to determine the geological structure. The

resulting bouguer anomaly profiles were shown in Figure. 3.

The bouguer value of profile SS-01 and profile SS-02 were

increase with offset from South-West (SW) to North-East

direction (NE). The trend of bouguer anomaly was present

the direction of basement laying down from NE to SW.


Suklim et al.

Figure 4 Interpreted resistivity profile (top) and seismic section (bottom) of survey line SS-01

Figure 5 Interpreted resistivity profile (top) and seismic section (bottom) of survey line SS-02.

RESISTIVITY SURVEY

The objective of the electrical surveys is to determine the

subsurface resistivity distribution from measurements taken

at the ground surface. Ground resistivity is related to various

geological parameters such as the mineral and fluid content,

porosity and degree of water saturation. Resistivity surveys

can provide data for subsurface geological and structural

interpretation, which can be used to detect and map fault

systems.

Resistivity measurements are acquired by injecting cur-

rent into the ground through two current electrodes and

measuring the resulting voltage difference at two potential

electrodes. The apparent resistivity distribution, ρa, is then

calculated from the input current, I , and output voltages, V .

The apparent resistivity can be written as (Telford, 1990):

ρa = kV

I(1)

where k is the geometric factor which depends on the

electrode arrangement.

In this study, the resistivity surveys were performed with

ABEM TERRAMETER SAS 4000 resistivity meter. The

dipole-dipole array was chosen based on previous work that

showed quite good resolution of fractures and fault with this

configuration (Liberty L. M., 2011, Adepelumi, 2008, and

Wise, 2003). The survey employed a dipole spacing of 10

m for SS-01 and SS-02 surveys lines. The raw apparent

resistivity dipole-dipole data were inverted and interpreted

using the rapid two-dimensional (2D) resistivity inversion

least squares method which developed by Loke, (1998), was

used to acquire a 2D ’true ’ earth resistivity inversion solution

in a color grid. The results of the resistivity survey can

be displayed as apparent vertical resistivity cross-sections

along the survey lines. The results indicate the existence of

fault anomalies, as illustrated in Figure 4 and 5 (top). The

faults appear as low-resistivity features in higher resistivity

environments and elevation changed of high resistivity value.

SS-01 and SS-02 profiles shown the low resistivity zones (red

dash lines) are considered as faults. A resistive layer (higher


Geophysical surveys to detect potential active faults

than 500 ohm.m) was interpreted as a layer, below a depth of

7-10 m of the profile.

SEISMIC REFLECTION SURVEY

The basis for seismic survey is wave propagating through

earth structure, and are scattered up to the surface where

they are measured. Seismic imaging then takes this data and

produces a reflectivity map of the structure that generated

the data. Propagation waves change in character during its

propagation which depend on the physical equation solutions

that contain much useful such as amplitude, period and

phase (Throner, 2001). The propagating wave character

also depends on how it is travelling. For example, surface

waves travel along a free surface of interface between two

media, while body waves traverse through a medium ’s body.

Measuring the character of one or all propagating wave help

reveals the medium ’s properties. The reflection method

involves recording seismic wave that are reflected off of

layers in the subsurface. In reflection theory it is important

to recognize that the angle of incidence is equal to the angle

of reflection. In general, seismic waves travel down and

reflect up to the array of receivers on the Earth ’s surface.

The velocity of the subsurface can be calculated based on

the arrival time of the waves. The reflecting waves from

hyperbolic curves are used to estimate velocity in reflection

processing and construct subsurface structure model. In this

study the seismic reflection survey was set up at the same

line of the resistivity survey (Figure 2). The data acquisition

parameters are shown in Table 1. Data processing steps

applied to the seismic data flow by:

(i) Header correction is used to convert input file from SEG2

to SEGY format.

(ii) Geometry is to input information such as shot and

receiver stations and offset.

(iii) Amplitude gain is applied to enhance weak signals.

(iv) Edit is to kill and mute bad seismic traces.

(v) Elevation and refraction static corrects effects of source

and receiver elevations.

(vi) Band-pass filter removes noisy frequencies such as

ground roll and high-frequency ambient noise.

(vii) F-k filter is used to enhance the signal-to-noise ratio by

attenuating coherent noise.

(viii) Deconvolution is to compress the wavelet components

and eliminate multiples.

(ix) Sort converts shot gathers to CMP gathers.

(x) Velocity analysis determines suitable velocity of each

layer for NMO correction from the seismic data. The

computed velocities were input to the NMO operator.

(xi) NMO describes the effect of the separation between

receiver and source on the arrival time for non-dipping

reflectors.

(xii) Stack is to reduce random noise and to increase the

signal-to-noise ratio by combining of seismic traces of

Table 1 Acquisition parameters of the seismic survey.

Item Parameters

Source EWG accelerates weight drop

Receiver 28Hz vertical single geophone

Seismograph 48 channels Geometrics Strata view

SS-01 Seismic survey line 1 km

SS-02 Seismic survey line 1.2 km

Geophone spacing 10 m

Shot spacing 5 m

CDP spacing 2.5 m

Vertical stacks 2-4

Data format storage SEG2

Sample rate 0.25 ms

Recorded length 4 s

Maximum fold coverage 52

the same position.

(xiii) Migration improves the resolution by focusing of the

energy by collapsing point diffractions to one point and

adjusting location and dip of layers.

The velocity depth relationship revealed distinct layers

that were controlled by fault structure. Profile SS-01 is 1 km

long. On the basis of velocity change with depth, the profile

has four layers. The average velocity of the top layer is about

750 m/s. The second layer has an average velocity of 1500

m/s. The third layer has an average velocity of 2000 m/s.

The bottom layer is characterized by a high velocity of more

than 2300 m/s of the upper part of the rock basement. And

profile SS-02 is 1.2 km long. The seismic section shows five

layers. The average velocity of the top layer is about 800

m/s. The second layer has an average velocity of 1800 m/.

The third layer has an average velocity of 2000 m/s. The

fourth layer has an average velocity of 2200 m/s. The bottom

layer is characterized by a high velocity of more than 2500

m/s. These layers have discontinuous reflector, because it is

cross-cut by many fault structures as shown in Figure 4 and

5(bottom).

DISCUSSION AND CONCLUSION

The structural imaging using geophysical surveys in poorly

stratified semi-consolidated sediments is very challenging,

the seismic reflection profile reveals two sets of normal

faults: the first group cutting through the lower part of both

sections and the second group cutting only the upper part of

the sections. Most of the faults are not vertically connected

with only some of the older faults propagated upward into

the upper section. An interval of very low resistivity anomaly

representing a shallow layer was cut across by a number of

faults in the profile. A number of faults appear to the surface,

that they are appropriate candidates for being the potential

active faults relate to recent earthquake activities in the area.


Suklim et al.

This potential active fault system should be studied

in more details to confirm its geometry, orientation and

lateral extent. More seismic survey lines across the possible

unexposed faults in the Chiang Mai Basin should be acquired

along with other geophysical methods (e.g. ground penetrat-

ing radar, resistivity, gravity, magnetic, electromagnetic, soil

gas radon). Integration of such data will be very useful for

city planning and mitigation of possible earthquake hazard

related to the movement of this fault system

ACKNOWLEDGEMENTS

We thank the PTTEP, Thai Center of Excellence in Physics

(ThEP), and the Graduate School, Chiang Mai University for

research funding supports.

REFERENCES

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Awoyemi, M. O., & Falebita, D. E., 2008. Integrated geophys-

ical mapping of the ifewara transcurrent fault system, nigeria,

Journal of African Earth Sciences, 52, 161–166.

Kearey, P., Brooks, M., & Hill, I., 1991. An introduction to

geophysical exploration, Blackwell Science, London, UK.

Liberty, L. M., Wood, S., van Wijk, K., Hinz, E., Mikesell, T. D.,

Singharajawarapan, F., & Shragge, J., 2011. The establishment

of a geophysics field camp in northern thailand, The Leading

Edge, 30, 414.

Loke, M. H., 1998. RES2DINV version 3.3: Rapid 2D resistivity

and IP inversion using the least-squares method: Computer disk

and manual, Penang, Malaysia, Applied Geophysics.

Petersen, M., Harmsen, S., Mueller, C., Haller, K., Dewey, J., Luco,

N., Crone, A., Lidke, D., & Rukstales, K., 2007. Documentation

for the southeast asia seismic hazard maps, U.S. Geological

Survey Administrative Report September 30, 30, 65.

Telford, W. M., 1990. Applied Geophysics, Cambridge University

Press, Cambridge.

Throner, R. H., 2001. Engineering geology field manual, U.S.

Department of the Interior Bureau of Reclamation (USDIBR).

Wise, D. J., Cassidy, J., & Locka, C. A., 2003. Geophysical imaging

of the quaternary wairoa north fault, new zealand: a case study,

Journal of Applied Geophysics, 53, 1–16.


Thailand Crustal Thickness Estimation Using JointInversion of Surface Wave Dispersion and ReceiverFunctions

Tira Tadapansawuta,∗, Siriporn Chaisria,b, Paiboon Nuanninc

a Student (M.Sc.), Department of Geology, Faculty of Science, Chiang Mai Universityb ThEP, Commission of Higher Education, 328 Si Ayuthaya Road, Bangkok 10400, Thailandc Department of Physics, Faculty of Science, Prince of Songkhla University


ABSTRACT

Surface wave has dispersive characteristic, phase velocity variation with frequency, which depends on the layer properties of subsurface,

shear wave velocities and layer’s thickness. The advantage of surface wave dispersion method can provide high sensitivity of average

local velocity. The receiver functions are time series, computed from three-component seismograms, which show the relative response of

Earth structure near the receiver. Modeling the amplitude and timing of those reverberating waves can supply valuable constraints on the

underlying geology. Therefore, the receiver functions method can provides ability to indicate boundary between crust and mantle or the

Moho boundary. The joint inversion of surface wave dispersion and receiver functions is the method that uses the advantage obtained from

surface wave dispersion and receiver functions to create optimizing local crustal thickness map. The earthquake data were selected with

the magnitude greater than 5 and epicenter distance between 20 to 40 for surface wave dispersion and 30 to 90 for receiver functions,

occurred during 2008 to 2011 and detected by 15 seismic stations of Thai Meteorological Department Seismic Network (TMDSN). The

global velocity model (AK-135) is used as initial model for surface wave dispersion inversion and then the model resulted from that is used

as the initial model for receiver functions inversion. The results show that the crustal thickness beneath Thailand is respectively thicker from

S-W part, with average thickness 20-30 km, to N-E part, with average thickness 30-45 km. Although the joint inversion provides better

resolution than the other methods, the obtained model resolution is not much more than that in the previous research such as the structure

of crust and upper mantle beneath northern Thailand by Pacharapongsakun (Pacharapongsakun, 2006) and Thailand crustal thickness by

receiver functions method (Wongwai, 2010), because there are not many high signal to noise ratio of earthquake signals.

KEYWORDS: Receiver Functions, Surface Wave Dispersion, Seismic Earthquake, Joint Inversion, Crustal Thickness

INTRODUCTION

General of Thailand crustal thickness

The location of Thailand covers 7.5 to 20 N latitude

and 98 to 106 E longitude, and there are two micro-

plates in Thailand as Shan-Thai and Indochina. In the early

stage of their evolution (Archeotectonics), Shan-Thai and

Indochina were cratonic fragments of Gondwana, Australia

in the Southern Hemisphere during the Precambrian to Lower

Paleozoic. During Middle Paleozoic to Lower Triassic (Pale-

otectonics), Shan-Thai and Indochina were rifted and drifted

in the Paleotethys. Paleomagnetic and Paleontologic data

indicate that Shan-Thai moves from a low latitude Southern

Hemisphere to a low latitude Northern Hemisphere position,

while rotating is nearly 180 degrees in the horizontal plane,

in the time between early Carboniferous and early Triassic.

During the Middle Triassic, Shan-Thai sutured nearly simul-

taneously to Indochina and to South China, the continent-

continent collision being a part of the Indosinian Orogeny

and Indochina tended to underthrust Shan-Thai.

After the collision (Mesotectonics), the mountains arose

along the suture, particularly along the overthrusting Shan-

Thai margin, and at the same time granites were intruded

to high levels in the sediments, and extensive rhyolites were

extruded on the land surface. The erosion of the mountains

produced mollasse deposits (mostly alluvial plain red-beds)

which occur on both sides of the suture, but are most

fully developed in the Khorat Basin that are formed on the

underthrusting west side of the Indochina continent.

Rifting of continental Southeast Asia and the opening of

the Gulf of Thailand by the tensional regime during late Cre-

taceous to Tertiary marks the Neotectonics stage of Thailand


Crustal thickness using joint inversion of surface wave dispersion and receiver functions

with subsequent rapid uplift of the present mountains during

the Quaternary (Bunopas and Vella, 1983).

The collision of Shan-Thai and Indochina effects the

changing of crustal thickness beneath Thailand. Moreover,

there is biggest earthquake event on 26 December 2004 in

Sumatra Indonesia, the Shan-Thai plate was drifted to west

(Gahalaut et al., 2006). Therefore, the changing of crustal

thickness is expected.

The basicly data of Thailand crustal thickness is obtained

from global crustal thickness as CRUST2.0 model (Gabi

et al., 2011) which provides thickness in range 10km unit

interval. As a result, the thickness from global crustal

thickness is too rough for local crustal thickness study.

To make the high resolution of local crustal thickness,

one of geophysics method as joint inversion of surface

wave dispersion and receiver function, which use seismic

earthquake data for crustal study, is brought advantage of

both surface wave dispersion, which furnish average local

velocity, and receiver functions, that provide ability of crustal

boundary separation, for Thailand crustal thickness investi-

gation.

Surface wave dispersion

In this study, the surface wave is used for a part of the

joint inversion because surface wave is easy to recognize

from seismogram because of its high amplitude and low

frequency contents. It has a very distinguish characteristic

called velocity dispersion, each frequency travels with dif-

ferent velocity and low frequency waves travel faster than

high frequency waves (Stein and Wysession, 2003). The

dispersion characteristic of surface wave is depended on the

shear wave velocity in the layer media, the deeper layers

has higher velocity than the top layers. The low frequency

portion of surface wave can propagate deep into the earth’s

crust and that make it travels faster than high frequency

portions. Therefore, the dispersion of surface wave can be

used for investigating subsurface layer.

There are two types of surface wave which are Rayleigh

wave and Love wave. Rayleigh wave has displacement in

vertical and radial (direction from earthquake to the receiver)

components of the seismogram. On the other hand, particle

displacement of Love wave is in transverse component, in

horizontal and normal to the direction from earthquake to

the receiver. The dispersion characteristics from both types

of surface wave are similar. In this study, the investigation

will concentrate mainly on characteristics of Rayleigh wave

because it has displacement in vertical components, and more

convenience for the investigation (Warren et al., 2009).

The dispersion characteristic is presented in curve be-

tween frequency and phase velocity also called dispersive

curve. From seismogram, phase and amplitude spectrums

of seismic wave are calculated by Fourier’s transform. The

travel time and epicenter distance of each frequency are

Figure 1 Compare the assumption data position between using one

seismic station (a.) and using two seismic stations (b.)

used for calculating phase velocity. After calculating phase

velocities of each frequency, the dispersive curve can be

obtained and used to generate shear wave velocities profile.

In this study, the surface wave dispersion method uses

the adapted program by Herrmann (Herrmann et al., 2002)

that requires at least two stations in the same great circle

path for investigation. The dispersive curve calculated from

two stations is similar to that from one station method, but

the phase velocities are calculated from distance and time

intervals between two stations instead of calculated from in-

dividual station. If we use only one station for phase velocity

calculation, it can provide error from incorrect origin time

and epicenter distance. Therefore, the assumption position of

shear wave velocity profile is between both stations in Fig.1

(Reiji, 2000).

The two-layer simple model is shown in Figure 2a.

When β1 and β2 are the shear wave velocity of top and

bottom layers, respectively. For surface wave to be existed,

β1 has to be less than β2. The surface wave velocity or phase

velocity (Cx) is limited in between the shear velocity in each

layer, between β1 and β2 .

For Love wave, the velocity dispersion can be modeled

as,

tan(ωH

Cx

√

C2x

β21

− 1) =ρ2β

22

√

1− C2x

β2

2

ρ1β21

√

C2x

β2

1

− 1(1)

where ω is angular frequency of Love wave, ρ1 is density

of the Earth’s crust, ρ2 is density in half-space, and H is

thickness of the layer medium. If the thickness is assumed

to be unknown parameter, it can be calculated from selected

phase velocity and its frequency on dispersive curve (Figure

2b). Then, these parameters are used for calculating by

modeling equation (Equation 1).

For real Earth, there are many layers in subsurface, the

dispersive curve is not a smooth curve or a simple curve.

The shear wave velocity model is computed by least square

inversion method. The initial model is needed for inversion.

The initial model is importance because the final model is

sensitive to the initial model. The shear wave velocity profile

from inversion can indicate the Moho (boundary between

crust and mantle). The characteristic of decreasing and then

increasing in shear wave velocity profile is used for indicat-


Tadapansawut et al.

Figure 2 a) 2-layers simple model (modify from Robert et al.,

2002) b) Example of dispersive curve plotted between phase ve-

locity and period

ing the position of The Moho (Trampert and Woodhouse,

1995). The problem of surface wave dispersion is hard to

separate the Moho for some areas where there are complex

structures because there are a number of shear wave velocity

changing, or the lower layer is thicker, and its shear wave

velocity is lower than top layer, therefore the geological data

is important and should be integrated into the consideration

for this method.

Receiver function

Receiver functions are time series, computed from three-

component seismograms, which show the relative response

of Earth structure near the receiver. Modeling the ampli-

tude and timing of those reverberating waves can supply

valuable constraints on the underlying geology. Often, the

main features of the structure can be approximated by a

sequence of nearly-horizontal layers. In that case, the arrivals

generated by each sharp (that is, sharp relative to the shortest

wavelength in the observations), see Figure 3.

The amplitudes of the arrivals in a receiver function

depend on the incidence angle of the impinging P-wave and

the size of the velocity contrasts generating the conversions

(Pms) and multiples (PpPms, PpSms). The arrival times

of the converted phase and multiples depend on the depth

of the velocity contrast, the P and S velocity between the

contrast and the surface, and the P-wave incidence angle, or

ray parameter (Ammon, 2010).

The mathematic of receiver function method is not more

complex than surface wave dispersion method because the

receiver function is the impulse response of the seismic

velocity structure underlying the seismic station when ex-

cited by an earthquake event. Whereas, the surface wave

dispersion method uses its dispersive characteristic along

traveling ray path of wave for the investigation. The re-

Figure 3 The receiver function diagram, (a.) the conversion of P-

to-S and the multiple of ray diagram, (b.) the responding time series

from ray diagram or receiver function, (c.) model of subsurface

layers, (d.) results of receiver function from the model for each

layer.

ceiver function is computed by deconvolution between the

radial component and the vertical component of seismic

data. The deconvolution can be made either in time-domain

or in frequency-domain, but it is easy and convenient for

frequency-domain because the wave function can directly

share or multiply in frequency-domain (Krebes, 1989).

Supposing P (t) is the direct P-wave of a teleseismic

event. When the traveling seismic signal reaches a seismic

velocity discontinuity of two homogeneous layers at an

oblique angle, it will split into P-wave and change to P-to-S

wave, and then the waves will respectively reach to the above

station.

The energy ration of P-wave to conversed P-to-S wave

depends on the incident angle. The closer incident angle

will provide high ratio. The energy ratio is presented in

amplitude of receiver function. Therefore, the used data or

the earthquake events must have epicenter distance in 30 to

90 degree. At this epicenter distance range, there will be the

phase conversion of wave at crustal boundary (the Moho). If

the epicenter distance is less than 30 degree, it is possible

that the considered wave do not pass the Moho. On the other

hand, if the epicenter distance is more than 90 degree, P-

wave will disappear because this range is shadow zone.

There are many parameters carrying by earthquake sig-

nal such as source of earthquake, the path of crustal structure

signal near the receiver. Let the origin signal in frequency-

domain from teleseismic earthquake event or wavelet is

E(ω), all the changing of impulse respond along the ray



path of earthquake wave is T (ω), the instrument respond is

I(ω), and response to local velocity contrast near the receiver

is H(ω), (Clayton and Wiggins, 1976). The approximated

recorded signal in vertical component is results of signal

deconvolution as

Z(ω) = E(ω)T (ω)I(ω). (2)

The signal of Z(ω) does not contain the changing local

velocity respond H(ω) because the teleseismic wave will not

perpendicularly pass crustal boundary beneath the station in

vertical direction.

Whereas the approximated signal in radial component at

the receiver which relate to the events location will be

R(ω) = E(ω)T (ω)I(ω)H(ω). (3)

In order to find the function which represents the local

velocity contrast, we must divide the radial component of the

seismogram (equation 3) by the vertical component (equation

2) which gives

H(ω) =R(ω)

Z(ω)(4)

The result in equation 4 is still frequency-domain, so the

inverse Fourier transform is applied to calculate the function

of h(t) and also called“the receiver function” which indicates

the velocity contrast on the ray path of the incoming seismic

data (see Figure 3b.).

If there is the 2-layer simple crustal model as Figure 2a,

the depth of crust can be calculated from equation as

tpms − tp = H

(

√

1

β2− p2 −

√

1

α2− p2

)

(5)

Where p is ray parameter, tpms is time of P-to-S phase

conversion wave, and tp is time of first arrival P phase

wave. Although the equation 5 can be used for calculated the

thickness layer, it is just simple model. In the real earth, there

are many layers under subsurface. Therefore, the least square

inversion is brought for calculated for crustal model. The

initial model of both surface wave dispersion and receiver

functions methods is very important because the calculated

final model will relate to the initial model.

In this study, the global velocity model as AK-135 is

used to be an initial model for both surface wave dispersion

and receiver functions processing.

Joint inversion of surface wave dispersion and receiver

functions

In2003, R.B. Herrmann and C.J. Ammon (Herrmenn and

Ammon, 2003) adapted and combined surface wave dis-

persion with receiver function for joint inversion to find

crustal thickness. The joint inversion between both methods

provides higher resolution in deep structure because they

bring the advantages from each method together for signal

analyzing. For surface wave, it provides high sensitivity of

average S-wave velocity in vertical direction. Whereas, the

receiver function provides position of phase conversion and

has ability to identify high velocity contrast position as the

Moho interface (Julia et al., 2003). The procedure of joint

inversion is same as receiver functions method, but the model

resulted from surface wave dispersion inversion is used as

initial model for receiver function inversion (see Figure 4).

We expect that an initial model from surface wave dispersion

provides S-wave velocity model better than using an initial

model from global crustal thickness because the calculated

initial model are nearly the local subsurface layers beneath

Thailand. Therefore, the final model of S-wave velocity from

joint inversion will provide better results than using either

receiver functions or surface wave dispersion.

METHODOLOGY

The earthquake data were selected with the magnitude

greater than 5 and epicenter distance between 20 to 40

for surface wave dispersion and 30 to 90 for receiver

functions, occurred during 2008 to 2011 and detected by

15 seismic stations of Thai Meteorological Department Seis-

mic Network (TMDSN). This study is separated for three

parts. First, the surface wave dispersion is set for 14 lines

processing which shown in Figure 5. Whereas, the receiver

functions use 13 seismic stations which presented in Figure

5 except MHMT and TRTT stations. For surface wave

dispersion, we used band-pass filter with frequency 0.02-0.1

Hz for dispersive curve calculation because the dispersive

curve looks clear and has high signal to noise ratio. Only the

fundamental mode or zero mode is selected for the inversion

because the fundamental model is easy and clear for mode

selection.

Second, the receiver function is calculated from de-

convolution of radial and vertical components. The raw

data are filter for frequency range 1-10 Hz. Then, the

receiver function is used for least square inversion for crustal

thickness investigation. The global velocity model as AK-

135 is used for both surface wave dispersion and receiver

functions inversions.

Finally, the joint inversion is done for the Thailand

crustal thickness investigation by using the calculated model

from surface wave inversion to be as an initial model for the

receiver function in the joint inversion method (see Figure

4). After getting the final model from all method, then the

velocity of S-wave velocity in range 4-5 km/s is decided

to be and the Moho boundary or crustal thickness. The

contour map of Thailand crustal thickness is created from

the thickness at its position.


Tadapansawut et al.

Figure 4 Work flow of joint inversion of surface wave dispersion

and receiver function method.

RESULTS

Surface wave dispersion results

The line processing 3rd is selected to be an example of

surface wave dispersion investigation. First the seismic raw

data are qualified by using trace merging, trend removing,

filtering, and trace cutting. The example of prepared seismic

data for dispersive curve calculation is shown in Figure 6a,

and the calculated dispersive curve is presented in Figure 6b

After getting the fundamental mode from calculated

dispersive curve, it is taken to inversion which uses global

velocity model or AK-135 model to be an initial model. The

output model or the final model is shown in Figure 7.

The selected crustal boundary position is decided from

S-wave velocity in range 4-5 km/s. The position of the

thickness is estimated at the middle of line processing (see

Figure 1). Thus, the Moho position for line processing 3rd is

at 38.4 km. We do all every lines processing, and the results

of crustal thickness around Thailand is shown in Table 1.

The contour map of Thailand crustal thickness from

surface wave dispersion method, shown in Figure 8, is

created from the thickness values and their positions. The

thickness of crust is respectively thicker from S-W to N-E

parts of Thailand, and the highest thickness is at middle of

Thailand near the KRDT station. The thickness looks strange

from the other results because the positions which used for

contour map creation are approximated positions by using

middle of line processing to be data position. Although, the

surface dispersion do not provide good crustal thickness map,

the obtained velocity model is better than global velocity.

Figure 5 The line processing of surface wave dispersion (dashed

line) using the earthquake data which pass at least 2 seismic stations

in same great circle path, and the seismic stations around Thailand

(triangle).

Line Passing Long. Lat. Thickness(km)

L1 PBKT CMMT 99.96 17.69 43.6

L2 KHLT MHIT 98.28 17.06 33.3

L3 CMMT KHLT 98.77 16.80 38.4

L4 CMMT TRTT 99.32 13.32 44.1

L5 MHIT MHMT 97.95 18.75 39.4

L6 SRDT MHMT 98.53 16.29 42.2

L7 CHBT MHMT 100.13 15.46 52.9

L8 CHBT SKLT 101.47 9.96 30.4

L9 CMMT SKNT 101.46 17.89 41.2

L10 PBKT UBPT 103.22 16.77 41.1

L11 PBKT KRDT 102.91 15.58 47.7

L12 PBKT CHBT 101.65 14.66 55.7

L13 UBPT SRDT 102.30 14.84 41.3

L14 CMMT CHBT 100.64 15.78 42.4

Table 1 Performance at peak F-measure

Receiver functions results

There are 13 seismic stations used for receiver functions

calculation. The receiver function processing starts from



Figure 6 The example of prepared seismic data for dispersive curve

calculation (6a.), and the calculated dispersive curve (6b) and its

fundamental mode (white dot).

qualify data such as trace merging, trend removing, trace

cutting, and filtering. Then the seismic traces in radial

and vertical components are brought to calculate receiver

function by using deconvolution. The processing results of

KHLT station is shown to be example (see Figure 9).

After getting receiver function, it is taken to least square

inversion by using global velocity model or AK-135 model

to be an initial model. The crustal boundary or the Moho is

selected by decided velocity in range 4-5 km/s. The results

from inversion is shown in Figure 10.

The crustal thickness results of other stations is pre-

sented in Table 2.

The crustal thickness values and their position is plotted

for crustal thickness contour map (see Figure 11). The results

show that the crust is shallow at S-W part of Thailand and

respectively increasing thickness to N-E part. Although, the

global velocity model or AK-135 model is used as initial

model for both surface wave dispersion and receiver func-

tions inversions, the crustal thickness result of receiver func-

tions looks better and smoother than surface wave dispersion

results because the receiver functions provide the thickness

value beneath the seismic station Therefore, the receiver

functions provide ability of crustal separation whereas the

surface wave dispersion furnishes the average local velocity.

Thus the joint inversion is made for better results.

Figure 7 The selected fundamental (top), and the calculated model

or output model from the inversion (buttom).

Joint inversion of surface wave dispersion and receiver

functions results

The joint inversion of surface wave dispersion and receiver

functions used the advantage of both surface wave dis-

persion, which provides better average local velocity, and

receiver function, which furnishes ability of crustal boundary

separation. The joint inversion method is the same method

as receiver functions, but the S-wave velocity model from

surface wave dispersion is used to be an initial model. Thus,

the nearest data point of surface wave dispersion results is

used to be an initial model for each station. Moreover, the

receiver functions data from receiver functions method are

used to be the data for the joint inversion method. To clear the

procedure of joint inversion method, please see the work flow

in Figure 4. To illustrate processing results, the data from

KHLT station is selected to be example results (see Figure

12).

The results from joint inversion method present ability

of classification the crustal boundary, and provide better

sensitivity of S-wave velocity profile than only using receiver


Tadapansawut et al.

Figure 8 The crustal thickness beneath Thailand by using surface

wave dispersion method.

Figure 9 The example of receiver function processing result from

raw data (a.), qualified signal (b.), and calculated receiver function

(c.).

Figure 10 The velocity model of KHLT station (bottom) by using

4 receiver functions for the inversion (top).

Station Number of Longtitude Latitude Thickness

RF∗ (degree) (degree) (km)

CMMT 5 98.9476 18.8128 37.7

MHIT 23 97.9632 19.3148 37.4

PBKT 4 100.9687 16.5733 40.6

SRDT 8 99.1212 14.3945 38.2

KHLT 4 98.5893 14.797 42.3

KRDT 15 101.8442 14.5905 38.1

CHBT 7 102.3297 12.7526 42.5

UBPT 8 105.4695 15.2773 47.8

SKNT 9 103.9815 16.9742 40.7

RNTT 8 98.4778 9.3904 23.3

SURT 12 98.795 8.9577 25.7

PKDT 8 98.335 7.892 24.5

SKLT 7 100.6188 7.1735 23.3

Table 2 The crustal thickness from receiver functions

method and their locations

functions. The Moho boundary is selected from shear

velocity in range 4-5 km/s as both surface wave dispersion

and receiver functions considerations, or considering from



Figure 11 The crustal thickness beneath Thailand by using the

receiver functions method.

Station Initial model Number Long. Lat. Thickness

from SURF∗ of RF∗ (degree) (degree) (km)

CMMT L3 23 98.9476 18.8128 36.5

MHIT L5 2 97.9632 19.3148 34.3

PBKT L9 4 100.9687 16.5733 41.5

SRDT L13 8 99.1212 14.3945 34.0

KHLT L3 4 98.5893 14.797 33.9

KRDT L10 15 101.8442 14.5905 30.9

CHBT L12 7 102.3297 12.7526 42.2

UBPT L10 8 105.4695 15.2773 37.9

SKNT L10 9 103.9815 16.9742 39.7

RNTT L4 8 98.4778 9.3904 25.9

SURT L4 12 98.795 8.9577 23.4

PKDT L4 8 98.335 7.892 25.9

SKLT L4 7 100.6188 7.1735 23.7

Table 3 The crustal thickness results and their locations of

the joint inversion

extremely velocity increasing. The results of joint inversion

method are presented in Table 3.

CONCLUSION AND DISCUSSION

Thailand crustal thickness can be investigated by using

the joint inversion of surface wave dispersion and receiver

functions. This method provides satisfying results because

it applies advantages of both surface wave dispersion giv-

ing high sensitivity of local velocity models and receiver

functions furnishing ability to indicate the Moho boundary.

The comparison between the joint inversion and the other

works presents that is reliable because there are similar

crustal thickness trend with the other method. The crustal

thickness beneath Thailand is between 30 km to 50 km

depth. The highest thickness is at CHBT station with 42.2

km depth, and the thinnest is at SURT station with 23.4 km

depth. As a result, the highest and the shallowest thickness

from joint inversion are similar as the crustal thickness of

Wongwai study in 2010. Moreover, the crustal thickness,

being about 46 km depth, beneath Nan suture has same result

as Pacharapongsakun in 2006 which provide the anomaly

depth at 40-50 km. From these reasons, Thailand crustal

thickness by using joint inversion of surface wave dispersion

and receiver functions is believable. Moreover, if there

are many seismic stations and many high signal to noise

ratio seismic earthquakes, the resolution of crustal thickness

beneath Thailand will be increased. Finally, we expect that

the crustal thickness beneath Thailand study will give the

benefit to other researches.

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Evaluation of TMD Seismograph Network DetectionCapabilities

Chatupond Munkonga,∗, Paiboon Nuannina

a Geophysics Research Center, Department of Physics, Faculty of Science, Prince of Songkla University, Hatyai , Songkhla ,


ABSTRACT

The background seismic noise characteristics of 40 stations of Thai Meteorology Department Seismograph Network (TMDSN) were

analyzed by using power spectral density (PSD) estimation and their corresponding probability density functions (PDFs). The results

were directly evaluated with the new Peterson’s low and high noise model. The stations that closely located to a reservoirs reveal high

cultural noise level with periods < 1 s. The microseism noise (1 - 16 s) among the inland stations was very similar and uniform trend

and high noise level observed for the stations situated near sea shore. Variations of background noise for long period (> 20 s) varied from

-160 dB to -110 dB and higher than upper limited of the noise model from some stations due to poor quality of seismometer installation.

Sensitivity of the network, i.e. magnitude of completeness (Mc) was investigated by using TMD earthquake data from 1998 to 2010.

The data were separated to three periods following historical upgrading of the network. The analysis result gives Mc 5.3, 5.4 and 4.7,

respectively. Epicenter locations reported in TMD catalog were compared with the reference events from ISC catalog to estimate the

accuracy of epicenter location. The distributions of mis-location distance for each period are 25, 30 and 10 km. respectively.

KEYWORDS: Background seismic noise, TMD seismograph network, Magnitude of completeness, epicenter

INTRODUCTION

Thailand Seismograph Network is operated by the Seismo-

logical Bureau, Thai Meteorological Department (TMD) for

earthquake monitoring in Thailand and surrounding area.

The area of responsibility is cover within latitudes 0° -

25° N and longitudes 90° - 110° E. The earthquake ob-

servation in Thailand has started in 1963, the first World-

Wide Standardized Seismograph Network (WWSSN) was

deployed at Chiangmai province, and two years later at

Songkhla province. In 1997, one SRO station (Seismic

Research Observatories) was also installed at Chiang-mai by

the USGS (Nuannin, 1995). In the following years, short

period seismographs were installed in nationwide in Thailand

by Thai Meteorological Department (TMD) and Electricity

Generating Authority of Thailand (EGAT). There were 14

analog stations (SPZ 1 Hz), later added to 11 digital stations

(L-4-C3D and CMG -40 T) and begin reported earthquakes

in TMD catalogue in 1998.

After the great earthquake M = 9.1 on December 26,

Thai Meteorological Department has received funds to up-

grade and improve new weak motion and strong motion

stations throughout the country. The improvement of the

seismograph network project was divided into two phases.

The 1st phase started in 2004 and finished in 2006, 15 new

digital stations were deployed; consist of 8 short period sta-

tions (Trillium 40 SP) and 7 broadband stations (Trillium 120

SP). And the second phase was implemented during 2006-

2009; amount of 25 stations were installed; consist of 15

short period stations (S-13J, 1Hz) and 10 broadband stations

(KS-2000, 120sec). Altogether, The TMDSN consists of 40

seismograph stations throughout the country. Figure 1 show

the location of the stations.

NOISE ANALYSIS

The background seismic noise of year 2010 of each individ-

ual among 40 seismograph stations in TMDSN were esti-

mated by using PDF-SA solfware by McNamara and Boaz,

(2005). Upon processing, the time domain are converted into

frequencies domain and calculated Power Spectral Density

(PSD) via direct Fourier transform or Cooley-Tukey method

(Cooley and Tukey, 1965) For the entire available dataset,

which is divided into 1 hr time segments overlapping by

50 percentages. The instrument response was removed and

the PSD estimate to obtain accelerations. Details on this

calculation can be obtained from McNamara and Buland

(2004). The estimated PSD results were directly compared

to the high and low noise models (NHNM, NLNM) of


Evaluation of seismograph network detection

Figure 1 Distribution of seismograph stations in TMDSN.

Peterson (1993), that powers expressed in decibels referred

to 1(m/sec2)2/Hz.

To achieve a better analysis the background seismic

noise at individual stations, the Probability Density Func-

tions (PDFs) is calculated. For each period, a histogram rep-

resents the number of occurrences of each power bin. PDF in

percentile over a period versus power graph is represented by

color palette. More details of PSDs calculation can be found

in McNamara and Buland (2004).

Statistical analysis (max, mode, min, PDFs) also

displyed for each station. Mode curves is considered better to

represent the majority noise level and correspond to highest

probability density function at each frequency bin (Diaz et

al., 2010).

CHARACTERIZATION

The seismic background noise of all stations in the TMDSN

were displayed by PSD mode line curve in Figure 2. The

PSD extends from -165 dB to -100 dB to covering about

65 dB of power in high frequencies. At short periods (0.1

- 1 sec) the most major sources of noise are the human

activities (road traffic, machinery) that couple energy into

the Earth. This so-called cultural noise propagates mainly as

high-frequency surface waves that attenuate within several

kilometers from the source (Havskov and Alguacil, 2004).

The PDF histogram revealed high seismic background noise

level in many cases of the stations have been located nearly

reservoir i.e. CHBT, PKDT, SKNT, SRDT, CRAI, NONG,

PHIT, SURI, SRAK and KRAB shown high noise level

at high frequency band (1-15 Hz) (see Figure 3a). In

addition, among 4 stations have been situated closely to

local meteorological building i.e. CMAI, KHON, PATY and

UMPA displayed critical high background noise at frequency

around 10 Hz (see Figure 3b). However, all of the PSD curve

still below the High Noise Model.

Figure 2 PSD mode curve of seismic background noise levels of

all stations in TMDSN.

In microseisms (1-16 sec), the background seismic noise

has been relationship with the energy released by oceanic

waves in this interval (Diaz et al., 2010). Therefore, the sea

shore stations in TMDSN i.e. PKDT, KRAB, SKLT, TRTT,

RNTT, SURT, SURA and SRIT were specially considered.

The PDF histogram indicated the highest seismic background

noise level in case of PKDT station (see Figure 3c). It

reaches to -120 dB (at 4-8 sec), However, It still lower to the

High Noise Model. These more effected by oceanic noise

because the station is located on island. However, the PSD

of all stations lie within -170 dB to -120 dB to cover 50

dB of power and just above 20 - 30 dB to the Low Noise

Model. Therefore, all stations can be considered as good

performance for noise period 1 - 16 sec.

In case of long period (T > 16 sec), the PDS show

extremely different between high and low of seismic back-

ground noise levels. The low PSD lie about -170 dB to -

180 dB caused by data from short period stations in TMD


Munkong and Nuannin

Figure 3 The PDF spectrum of TMD seismograph stations. (a) High seismic background noise at short period (0.1-1 sec) of stations

that located nearly reservoir. (b) High seismic background noise at short period (0.1-1 sec) of stations located nearly local meteorological

building. (c) Microseism background noise of sea shore stations.



phase II. However, the rest of PSD show covering seismic

background noise from -160 dB to -110 dB and among 9

stations i.e. TRTT, CHBT, SRDT, SURT, CMAI, NAYO,

CRAI, SURA and PHRA show the PSD lie above the High

Noise Model. Moreover, the PDF histogram displayed

variations of seismic background noise in many case of

stations such as CRAI, NONG and TRTT (see Figure 3c).

Therefore, the reason for increased long period noise may

be air circulation in the seismometer vault or underneath the

sensor cover (Borman, 2002). these stations are considered

as poor performance for noise in long period.

SENSITIVITY OF NETWORK

Earthquake frequency-magnitude relationship is a way to ex-

amine seismic activity in an area. The Frequency Magnitude

Distributions (FMD) describes the number of earthquakes

occurring in a giving region as a function of their magnitude

Gutenberg and Richter, 1994) which is given by:

log10(N) = a− bM, (1)

where N is the cumulative number of earthquakes greater

than or equal to magnitude M , and a and b are real constants

that may vary in space and time.

A critical parameter for seismicity and hazard related

studies is the magnitude of completeness value; Mc (Wiemer

and Wyss, 2000). Therefore, in order to evaluate the per-

formance of the network, the completeness magnitude was

considered to determine sensitivity or minimum magnitude

that the network that can be detected completely based on a

linearity assumption of the cumulative FMD equation. The

TMD earthquake data during 1998-2001 was rearranged and

divided into three periods, following significant improve-

ments of TMD seismograph network. Figure 4 shows the

correlations between cumulative number of earthquake and

magnitude scale of each network periods. The Mc point

can defined as the magnitude at which a graph departs

from the linear range. The frequency-magnitude relationship

curve shows a completeness magnitude of 5.3 for located

earthquakes (TMD Bulletin) in the period 1998-2006 (Figure

4a) and 5.4 for located earthquakes in the period 2006-

2008 (see Figure 4b). These similarities of a completeness

magnitude might be caused by low of seismograph stations

in network. However, the frequency-magnitude relationship

curve in period 2008-2011 displayed bimodal of a com-

pleteness magnitude (Figure 5a). Therefore, the earthquakes

occurred in this period were separated by epicenter locations

for better to analyze a completeness magnitude of network

as correlations area. The resulted shown a completeness

magnitude as 4.7 for latitude 0-15° N (see figure 5b) and

3.2 for latitude 15-25° N (see Figure 5c). The intensity of

seismographs in the last period of network improvements

were paramount parameter caused to better a magnitude of

completeness both for regional and local earthquake events.

Figure 4 Cumulated Gutenberg-Richter distribution from TMD

bulletin. (a) Data since January 1998 to September 2006. (b) Data

since October 2006 to October 2008.

LOCATION ACCURACY

International Seismological Network (ISC) has been organi-

zation to compile earthquake data from over 130 agencies

worldwide and on-line bulletined. Reviewed ISC bulletin has

been recalculated by ISC analysts and available utilization

data for 24 months behind real-time event. Epicenter infor-

mations form those agency were analyzed and relocated by

using ISC location algorithm.

In order to evaluate the location accuracy of TMDSN,

earthquake locations were compared with the ISC reviewed

bulletin. Data were divided into three parts to follow the

significant improvements network. The location error shown

by distribution histogram of mislocation distance. The most

population data of mislocation distance displayed highest

frequency count histogram bar, with level of 25-30 km in

the period 2004 to 2005,with 30-35 km in the period 2007 to

2008 and with 5-10 km in the period 2009 to 2010 ( figure

6). The results indicated similar values in the first and second


Munkong and Nuannin

Figure 5 FMD of TMD bulletin during November 2008 to December 2011. (a) bimodal cumulated curves. (b) Cumulated curve for

latitude 0-15 N, 90-110 E. (c) Cumulated curve for latitude 15-25 N, 90-110E.

periods. However, the mislocation value drop down in the

last period. It considered acceptable value for location error

but still shown a long tail histogram. The reason for support

those results might be more intensity stations in TMDSN in

the last period.

Nevertheless, the statistical analysis show higher average

value than previous mode histogram in all the periods. More-

over, the standard deviation shows high value as indicated

high distributions error of epicenter location. Therefore,

absence of the local velocity of Thailand has been considered

as important reason for poor locations earthquakes.

CONCLUSIONS

The performances of TMDSN were analyzed by significant

parameters i.e. background seismic noise, sensitivity of

network and accuracy of epicenter location.In first section,

the seismic background noise of individual seismograph

station was calculated. The PDF reveal high noise level

in many cases of stations nearby the reservoir and a local

meteorological station at short period. However, The PSD of

all stations still below the HNM curves by Peterson (1993).

In microseism noise, oceanic noise do not influences to

sea shore station’s performance, all of them have a good

performance for this periods. For long period, the PDF

of 9 stations shown higher the HMN that considered as

poor performance stations. Moreover, variations of seismic

background noise were found in many cases of stations due

to poor quality installation sites.

The next section, frequency magnitude distribution were

plotted to determine the sensitivity of the network. The

results indicate that the completeness magnitude of each

period network are 5.3 and 5.4 for 1998-2006 for 2006-2008,

respectively. However, the bimodal FMD was achieved for

the period of 2009-2011, i.e. Mc = 4.7 for the seismicity

in the southern part and Mc = 2.7 for northern Thailand.

This means that earthquake magnitude less than 4.5 in the

Sumatra-Andaman and southern Thailand cannot completely

recorded by TMDSN. We suggest that installation more

dense stations of high gain seismograph in the south are

necessary to get a better sensitivity which is very important

for micro earthquake and aftershock observations.

Finally, epicenter location reported in TMD catalog were

compared to reference events from the ISC bulletin to inves-

tigate location accuracy of the network. Histogram of mis-



Figure 6 Histogram of mis-location distances of TMD catalog versus reviewed ISC bulletin. (a) For first the period 2004-2005. (b) For

second period 2007-2008. (c) For third period 2009-2010.

location distances shown highest numbers at 25-30 km for

2004-2005 30-35 km and 5-10 km for 2007-2008 and 2009-

2010, respectively. The highest deviation of the epicenter

location is in the period of the second phase deployment.

Although, the lowest mis-location after the second phase

is satisfied for teleseismic earthquakes i.e. within ±30 km,

the local velocity model should be used to estimate epicenter

location for better result.

ACKNOWLEDGMENTS

The authors would like to thank the Graduate School, Prince

of Songkla University (PSU) for partial financial support

for this research and Thai Meteorological Department for

providing the earthquake data for this study. The authors

thank U.S. Geological Survey (USGS) to permit us use

software for analysis seismic background noise.

REFERENCES

Bormann, P., 2002. New Manual of Seismological Obser-

vatory Practice (NMSOP), GeoForschungsZentrum, Potsdam,

Germany.

Cooley, J. W. & Tukey, J. W., 1965. An algorithm for machine

calculation of complex Fourier series, Math. Comp., 19, 297–

301.

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tics at the IberArray Broadband Seismic Network, Bull. Seism.

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California, Bull. Seism..

Havskov, J. & Alguacil, G., 2004. Instrumentation in earth-

quake seismology, in modern approaches, in Geophysics Series,

Springer, Dordrecht, The Netherlands.McNamara, D. E. & Buland, R. P., 2004. Ambient noise levels

in continental United States, Bull. Seismol. Soc. Am., 4, 1517–

1527.

Nuannin, P., 1995. Seismicity of Thailand, Tech. rep., Seismologi-

cal Department, Uppsala University, Sweden.

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seismic noise, in U.S., Tech. rep., Geol. Surv., Albuquerque,

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Wiemer, S. & Wyss, M., 2000. Minimum magnitude of complete-

ness in earthquake catalogs: Examples from Alaska, the western

US and Japan, Bull. Seism. Am., 95, 684–698.


Microtremor measurements in Chiang Mai city, northernThailand for seismic microzonation

Narin Kluntonga, Passakorn Pananonta,∗

a Department of Earth Sciences, Faculty of Science, Kasetsart University, Bangkok, THAILAND


ABSTRACT

The Chiang Mai City is one of the biggest cities in the northern Thailand. The city is a part of the Chiang Mai Basin that is covert by fluvial

sediments. This soft sediment can both introduce its own natural frequency and can amplify the ground shaking from the earthquake.

The phenomena can lead to potential damages of the buildings and infrastructures in the city due to stronger shaking in the event of

the earthquake. The microtremor observation measures the vibration of the ground from the ambient noise and can be used to identify

the natural frequency of the ground shaking. The horizontal to vertical spectral analysis (H/V) is used to approximate both predominant

frequency and possible amplification of ground motion. The effect of soft sediment on seismic wave in the Chiang Mai city was studied

by measuring predominant frequency of the ground at 36 sites by microtremor observations. The predominant frequencies of the Chiang

Mai city range from 0.23 Hz to 4.95 Hz. The fundament frequency of the ground trends to be lower (longer period) towards the Ping River

which could be due to variations in sediment thickness. The result of this study can be applied for the seismic microzonation of the city in

the future.

KEYWORDS: Seismic Microzonation, Microtremor Observation, Chiang Mai, Seismic Hazard

INTRODUCTION

It is well known that the behavior of the ground motion

during an earthquake is generally well explained by the

lower velocity and density in the geological subsurface. In

1989, it was the first time that Nakamura used the single

site Horizontal to Vertical Spectral Ratio (HVSR) method to

study a site responses using ambient seismic noise sources.

The Chiang Mai city, one of the biggest city in the

northern Thailand, is covert by fluvial sediments that can

both create a specific natural ground response and can also

amplify the amplitude of the seismic wave. This phenomena

are important factors that can introduce potential damages of

the buildings and infrastructures in the city due to stronger

shaking in the event of the earthquake. There are numerous

examples of the damages to the city that was a result of

natural response of the ground and a soil amplifications such

as several damages from moderate earthquakes occurring in

California and the infamous Mexico city incident in 1985.

The natural ground shaking of a certain site can be obtained

with a microtremor method that measures ambient noise on

the ground surface. The main sources of these noises are

human activities, traffic and nature (such as ocean wave

and wind). Investigation of these microtremors can be used

to approximate both predominant frequency and possible

amplification of ground motion at a specific site.

The microtremor can be calculated with the empirical

formula using the measured ambient noise data of both

vertical and horizontal ground movement at the site of

interest. The results can be used in constructing a seismic

microzonation of the city. The proposed H/V spectral ratio

method, in which the predominant period of the ground

vibration is determined by the ratio of horizontal and vertical

Fourier spectra of microtremors observation at a site.

This study focuses on constructing a seismic micro-

zonation map of the Chiang Mai city using microtremor

observations. The study area (Figure 1) is located in the

urban area in the center of the city of Chiang Mai.

GEOLOGICAL SETTING

The surface geology of Chiang Mai city consists mainly of

fluvial sediments, with bedrocks cropping out at the western

part of the city (Doi Suthep mountain) The sediments are

Quaternary alluvium deposits of thin clay and sand along the

Ping river which flows through the northern and southern part

of Chiang Mai city. The measurement sites is located in the

center of the Chiang Mai city, which is a valley and fluvial

flood plain. Therefore this urban area can amplify the seismic

wave from the earthquake (Figure 2).


Microtremor for seismic microzonation

Figure 1: Study area of this work.

Figure 2: Locations of HVSR sites in the Chiang Mai city.

EQUIPMENT AND DATA ACQUISITION

Equipment

The ambient noise are recorded with a 3-component seis-

mometer with natural response from 40 second to 50 Hz

and a 24 bits digitizer. A sampling rate of 100 samples

per second were used. At each site, we record ambient

noise continuously for at least 90 minutes with GPS for the

accuracy control of the timing.

(a) A sample of spectrums of the microtremor measurement. Z, N

and E are vertical, N-S and E-W component respectively.

(b) H/V spectrum of recorded microtremor data.

Figure 3

Data acquisition

The microtremor data were recorded at 36 sites during

October through December 2009. Measuring locations was

selected to represent spatial distribution and to avoid direct

traffic, heavy machine, other underground structures and

strong wind or rain. The data processing was done with the

GEOPSY software.

RESULTS OF H/V MEASUREMENTS

The result of the H/V calculation suggest that the sites

in the central area of Chiang Mai city has low frequency

(long period) response, ranging from 0.23 - 0.99 Hz and the

amplification of ground motion are 2 - 6.4 times compared

to the rock surface, especially near Ping River. The result of

this study shown in Figure 4.


This study presents a result of predominant frequency and the

amplification factor from the microtremor measurement in

Chiang Mai city. It can be seen that sites with lower predomi-

nant frequency appear to locate towards the Ping River which

could contribute more soft sediment on the ground. The

result can suggest that the tall buildings near the Ping River


Kluntong and Pananont

Figure 4: Microzonation of the Chiang Mai city area on the

basis of variation of the predominant long period.

could have more effect from both a natural ground response

and site amplification. Although H/V method can provide a

rough estimate of the site amplification, it can be unreliable

due to biased noise source. The presence of noise generated

by localized movement such as traffic and pedestrians trends

to produce results which are not characteristic of the site,

but are characteristic of the energy source. Therefore the

H/V analysis has to be conducted carefully in order to obtain

accurate results.

In addition, to generate a mizrozonation map of such

city, the Vs30 value of the site will be needed to calculate

the site amplification accurately. The Vs30 can then be

combined with microtremor observations to generate a mi-

crozonation map of the city in the future.

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in the Bovec basin (NW Slovenia) related to 1998 Mw 5.6 and

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Resistivity imaging to detect the liquefaction induced bythe Mw 6.8 earthquake in Myanmar on March 24, 2011 inChiang Rai province, northern Thailand

Rapeeporn Sakulneea, Passakorn Pananonta,∗

a Department of Earth Sciences, Faculty of Science, Kasetsart University, Bangkok, THAILAND


ABSTRACT

On March 24th, 2011, an Mw 6.8 earthquake occurred in the Shan state, Myanmar. The epicenter was located at about 30 km north

of Amphoe Mae Sai, Chiang Rai province near the Thailand-Myanmar border. This earthquake generated the ground shaking that can

be felt throughout the northern Thailand and induced liquefaction along the northern border of Thailand. Eighteen lines of resistivity

imaging surveys (Dipole-Dipole and Wenner-Schlumberger arrays) were conducted in 5 study areas in Amphoe Mae Sai and Chiang Saen,

Chiang Rai province in order to evaluate the ability of the geophysical method to detect the liquefaction and to study the characteristic

of the liquefaction such as the depth that the liquefaction originally occurred. The results of resistivity imaging surveys indicate that the

liquefaction zones are represented by the high resistivity regions (∼100 - 200 Ωm) embedded in the low resistivity zone (less than 100

Ωm). These high resistivity anomaly could be the liquefied clean sands that are ejected along the subsurface ruptures upwards to the

ground due to the strong shaking. The low resistivity zone may be clay layers as the saturated clay has rather low resistivity values.

KEYWORDS: liquefaction, earthquake, resistivity imaging, Chiang Rai, northern Thailand

INTRODUCTION

The earthquake of moment magnitude (Mw) 6.8 occurred in

Myanmar on 24th March 2011. The epicenter was located to

the east of Shan State in Myanmar with a hypocenter depth

of 10 km (Figure 1). This earthquake occurred on an active

fault that is part of a broad zone of deformation resulting

from the collision of the Indian plate with the Eurasian plate.

The earthquake killed at least 74 people, injured 111 locals,

damaged 413 buildings and caused one bridge to collapse in

Shan state, Myanmar. It was felt widely in Myanmar, Laos,

southern China, Vietnam and northern Thailand. In Thailand,

one person was killed in Amphoe Mae Sai, Thailand.

The ground shaking from this earthquake was felt

throughout the northern Thailand, including Mae Hong Son,

Chiang Rai, Chiang Mai, Lamphun, Lampang, Nan, Phayao,

an as far as Nonthaburi and Bangkok.

This earthquake caused damage along the Thailand-

Myanmar border (Figure 2). We choose Amphoe Mae Sai

and Chiang Saen, Chiang Rai province as our study areas

as it is the first time that the liquefaction was evident.

We conducted several geophysical methods including the

resistivity imaging surveys in order to detect the liquefaction

and to study the characteristic of the liquefaction.

Figure 1 Showing a location of earthquake epicenter occurred in

Myanmar near the Thailand-Myanmar border. (Source: USGS,

2011)

Four sites of the resistivity imaging surveys were con-

ducted in Amphoe Mae Sai and one site were conducted in

Amphoe Chiang Saen (Figure 3). Near study areas is the

Sai river in Amphoe Mae Sai and the Ruak river in Amphoe

Chiang Saen which influence the local geology in these areas

including Quaternary floodplain deposits and river terraces,


Resistivity imaging to detect liquefaction

Figure 2 A photograph showing the liquefaction in Tambon Wiang

Hom , Amphoe Mae Sai, Chiang Rai.

Figure 3 A map showing the locations of the resistivity surveys.

which consist of clays sand and silts.

The first study area in Amphoe Mae Sai is at the Wiang

Horm Lawn (WHL) that includes 5 survey lines. The

appearance of the liquefaction at this site shows ruptures and

traces of the sands along the edge of the rupture. The sands

look different from those of the sand from the surrounding

environment. Another location is at the North Riverside near

Figure 4 A photograph showing the view from Lung Pun’s Farm

(LPF) study site. The cracks (ruptures) of the ground with liquefied

sands can be seen near the intersection of the survey lines.

Figure 5 A map showing the location of the survey lines at the

Lung Pun’s Farm (LPF) study site.

Wiang Horm lawn (WHN) which consist of 1 survey line.

This area is not far from first area and is locate near the Sai

river. The ruptures due to the liquefaction can be found in

this area as well. The third site is at the Wiang Horm road

(WHR) where a ground rupture ran across the middle of the

local road, which was about 1 m deep. One survey line were

conducted at this site. The last study site in the Mae Sai

is at the Wiang Horm rice Field (WHF) where two survey

lines were conducted. This area is filled with several small

ruptures and liquefied sands exist throughout the area. The

last study site was located in Amphoe Chiang Saen at the

Lung Pun’s Farm (LPF) which include 9 survey lines. This

site is the corn field located near the Ruak river’s bank where

ruptures and liquefied sands spread out in the field (Figures 4

and 5).


Sakulnee and Pananont

Figure 6 (a) A Wenner electrode configuration and (b) A dipole-

dipole electrode configuration.

RESISTIVITY IMAGING SURVEY

The purpose of electrical surveys is to determine the subsur-

face resistivity distribution by making measurements on the

ground surface and then the true resistivity of the subsurface

can be estimated. The resistivity of the ground depends on

various geological parameters such as the mineral, porosity

and degree of water saturation in the rock (M.H.Loke, 1999).

This study used a resistivity imaging with multi electrode

(48 channels) instrument. We choose two electrode configu-

rations for resistivity surveys: the Wenner-Schlumberger and

the Dipole-Dipole electrode configurations. The Wenner-

Schlumberger electrode configuration can provide a better

result to detect changes in resistivity with depth. The

electrode spacing is varied for each measurement, but the

center point of the array is constant (Figure 6a.). The

Dipole-Dipole electrode configuration can provide a better

detection of lateral variations in resistivity (Miisom, 2003).

For this configuration, the electrode spacing is fixed while

the center of the array is varied (Figure 6b.). Further details

on different array geometries and ranges of resistivity for

different materials (Table1) are given for instance in Telford

et al. (1990) and Reynolds (1997).

We conducted 18 profiles of electrical resistivity imaging

using a Syscal IRIS Instrument. 17 profiles have 2 m elec-

trode spacing resulting in 94 m long lines with a maximum

penetration depth of about 20 m. At the KWH study site, we

used 1 m electrode spacing that provides 47 m long lines.

Seven lines in approximately NE-SW, SW-NE directions

and 11 lines in NW-SE, SE-NW direction were conducted

(Figure 5 and Table 2).

RESULTS AND DISCUSSION

The data processing of the measured sets of apparent resis-

tivity were performed using the software RES2DINV. Figure

7 shows the result of the resistivity images from the survey

conducted at the LPF area.

The low resistivity zones from at depth to the surface can

be observed. The depth extent of these two zones appears to

be about 6 and 2 m, respectively. The resistivity values for

these zones are in the range of 100-200 Ωm, compared to a

background resistivity of less than 100 Ωm. These zones are

interpreted to be liquefied clean sands.

The results in WHL area that contain 5 survey lines show

the same trend. We could find high resistivity zones with 3-5

m thickness on most survey lines. We interpret these layers

as sand layers that have resistivties in the range of 100-250

Ωm. The bottom layer has low resistivity with ranges less

than 100 Ωm which may be clay. The result in the WHN

area show high resistivity zones with ranges in 108-200 Ωm.

The thickness of these layer are about 2 m. We interpret

that this area has sand layer laying above clay layer which

has rather low resistivity (less than 108 Ωm). The results

in the WHR area suggest a high resistivity zone with range

from 120 to 250 Ωm. and 2.5 m thick near the surface. The

low resistivity zone was interpreted as a thin clay layer with

resistivity values less than 100 Ωm. The result in the WHF

area suggest a thin sand layer above clay layer with thickness

of 4-5 m. The sand layer has resistivity range about 120-

300 Ωm and the clay layer has resistivity less than 120 Ωm.

Finally, The result in LPF area from 9 survey lines shows that

the resistivity ranges of the sand layer are 110-300 Ωm with

2-3 m thick. The clay layer has low resistivity that is less

than 110 Ωm.

CONCLUSION

The results presented in this study illustrate that resistivity

imaging method is a powerful tool to detect the liquefaction.

The results of resistivity imaging surveys can be used to

separate the sand layer (high resistivity) from the clay layer

(low resistivity) and can be used to identify the liquefied

zone. The result from this study suggest that the sand layer in

the study area have resistivity values of 100-300 Ωm and the

clay layers have resistivity value less than 100 Ωm. The low

resistivity zone may be clay layers as the saturated clay has

rather low resistivity values. These high resistivity anomaly

could be the liquefied clean sands that are ejected along the

subsurface ruptures upwards to the ground due to the strong

shaking.

ACKNOWLEDGEMENTS

We would like to thank The Faculty of Sciences and the

Kasetsart University Research and Development Institute

for the financial support for this work. The undergraduate

students at the Department of Earth Sciences, Faculty of

Science, Kasetsart University are also thanked supporting in

the geophysical survey.


Resistivity imaging to detect liquefaction

Figure 7 A result of the resistivity imaging from the line 3 survey conducted at LPF. The arrows indicate the location of the liquefaction

observed on the surface (a) The results from Wenner-Schlumberger configuration. (b) The results from Dipole-Dipole configuration.

REFERENCES

Loke, M. H., 1999. Electrical imaging surveys for environmental

and engineering studies - a practical guide to 2D and 3D sur-

veys, unpublished short training course notes, University Sains

Malaysia.

Miisom, J., 2003. Field Geophysics (The geological field guide

series), John Wiley & Sons.

Reynolds, J. M., 1997. An Introduction to Applied and Environ-

mental Geophysics, Wiley.

Sharma, P. V., 1986. Geophysics methods in geology, Prentice-Hall.

Telford, W. M., Geldart, L. P., & Sheriff, R. E., 1990. Applied

Geophysics, Cambridge University Press.

Zeyen, H., Pessel, M., Ledesert, B., Hebert, R., Bartier, D., M.,

S., & Lallemant, S., 2011. 3D electrical resistivity imaging of

the near-surface structure of mud-volcano vents, Tectonophysics,

509, 181–190.


Micro-tremor in Bangkok and its comparison withamplified shear waves and H/V spectrum of Rayleighwaves

Satoshi Morioa,∗, Yoshinori Katoa,, Akira Kitazumib,, Suwith Kosuwanc,, Sitirag Limpisawadc,,

Tirawat Boonyateed,

a Maizuru National College of Technology, Kyoto 625-8511, Japanb Ex JICA SV (Senior Volunteer), Osaka 592-0002, Japanc DMR (Department of Mineral Resources), Ratchatewee, Bangkok 10400, Thailandd Chulalongkorn University, Phayathai, Bangkok 10330, Thailand


ABSTRACT

Bangkok, the capital of Thailand, is located at a remote distance from seismic sources. However, it has a substantial risk from these distance

earthquakes due to the ability of the underling soft soil deposits to amplify ground motions. A wide range micro-tremor observation was

conducted in Bangkok metropolitan area to estimate the deep underground soil structure, velocity profile down to the seismic bedrock,

which is essential to predict long-period ground motion caused by the strong earthquakes. The long period velocity-type seismometer,

Trillium 40, was used. Micro-tremor observation was carried out at 89 sites in the greater Bangkok area during the winter season to detect

the long period micro-seisms caused by the high waves in the Gulf of Thailand. The horizontal-to-vertical (H/V) spectrum ratio of micro-

tremor was calculated and the subsurface velocity profile down to seismic bedrock was estimated. And these H/V spectrums were compared

with the SH wave amplification function and also with the theoretical Rayleigh wave amplification ratio H/V at the ground surface. It was

clarified that there was a deep basin at the southern part of Bangkok metropolitan area near the Gulf of Thailand. And it was also proved

that H/V spectrums of micro-tremor coincided with SH wave amplification function and also with theoretical Rayleigh wave amplification

ratio H/V at the ground surface very well.

KEYWORDS: Bangkok, Micro-tremor, H/V spectrum, Amplification function, Rayleigh Wave

INTRODUCTION

Recently, earthquake activities around Thailand were more

common. For instance, three M6 earthquakes occurred at

an active fault near to the western border with Myanmar

during last 50 years. M9.1 earthquake occurred at Sumatra in

December 26, 2004. Two M6 occurred near to the northern

border with Myanmar and Laos on May 16, 2007 and March

24, 2011. Although building failures or loss of life did not

occurred in Bangkok, people in high-rises could feel the

movement of buildings during these earthquakes. Bangkok

metropolitan is a big city with population of more than 6

million people. Besides high-rises in the city, a number of

large infrastructures have been constructed, for instance, the

Bangkok Transportation System (BTS), Mass Rapid Transit

(MRT), Suvarnnabhumi airport, Airport link line, etc. Since

earthquake activities were rarely observed in the past, few

structures were designed to resist earthquake motion. Since

Bangkok is situated on thick soil deposits, it is possible that

the magnitude of long distance earthquake motion can be am-

plified when it goes up to the ground surface. Unfortunately,

the influence of such layers to the amplification of waves

has not been fully understood. Therefore, even the seismic

design was carried out, the magnitude of ground motion used

in the design might be underestimated. Studies by the authors

(Kitazumi 2005; Morio et al, 2007) showed that thick soil

layers under the city would amplify long period earthquake

motion so that a number of structures will be damaged if an

M7 earthquake (an earthquake with magnitude more than 7)

occurs at an active fault in the vicinity of Bangkok.

MICROTREMOR MEASUREMENT

The measurements were carried out during winter season,

between 14 December 2009 and 20 January 2010, because

the height of ocean waves will be the highest across the year.

The measurements were made at 89 locations over an area of


Micro-tremor and H/V spectrum of Rayleigh waves

Figure 1 Measurement points in Bangkok and vicinity area

200 sq.km in Bangkok and its vicinities (Figure 1). A triaxial

seismometer (Trillium40) having a flat phase and sensitivity

response between 15 Hz and 40 sec was used to record the

velocity of ground motion at each location over 1 hour period

at a sampling rate of 100 Hz.

Figure 2 shows the velocity time histories at station 8.

For each location, three quiet 1-minute sections were selected

from the full record, transformed to their power spectrum and

averaged. The averaged power spectrum was then filtered by

a Parzen window function (0.2 Hz band) and normalized by

the maximum magnitude of NS component.

The normalized power spectrum at station 8 is shown

in Figure 3. It can be seen that the response of all three

components are strong at around 0.4 sec. However, the

horizontal spectrum (NS, EW) have also distinct peaks at

around 0.7-0.9 sec and at around 3 sec.

To focus on the difference between horizontal and ver-

tical motion, a common technique is to represent the power

spectrum by their ratio or H/V spectrum (Nakamura 1989).

In this study, the H/V spectrum was obtained by

H

V=

sqrtPx + Py

Px

(1)

where Px, Py, Pz are NS, EW, and vertical components,

Figure 2 Velocity time history at station 8

respectively. Using eq. (1), the power spectrum in Figure

3 can be transformed to H/V spectrum as shown in Figure 4.

Once transformed, it can be seen that the strong horizontal

responses at a short period (0.7-0.9 sec) and at a long

period (3 sec) are more obvious while the peak at 0.4 sec

is suppressed. The same procedure was also applied to


Morio et al.

Figure 3 Velocity power spectra of station 8

Figure 4 H/V spectra at station 8, 45 and 67

records at other stations. As shown in the same figure,

strong responses were also observed at two distinct periods

at station 45 and station 67.

Based on H/V spectrum of all 87 locations, the peaks

of H/V spectrums occurred in two zones which are the short

period zone ranging between 0.5 to 1 sec and the long period

zone ranging between 2 to 6 sec. The periods where the peaks

of H/V spectrum occurred in these zones are shown in Figure

5 and 6. The bigger circles are used for the longer period. It

is noted that locations where the periods are longer than 4 sec

were enclosed and shown in figure 5. A deep basin structure

was expected for this particular area. Based on a study at

AIT (Arai & Yamazaki, 2003), the short period peaks had

a strong relationship with the thickness of a soft clay layer

near to the ground surface. It was reported that the dominant

period was shorter than 0.4 sec on the north of Bangkok and

gradually increased toward the southern part. For instance,

the dominant period in downtown area was at 0.8 sec and

Figure 5 Long period peaks

Figure 6 Short period peaks

ranged between 0.8 and 1.2 sec on the coastal area. A similar

trend was also in the current study, the 0.7-0.9 sec peaks were

concentrated between the downtown area and the coast line

on the south. The thickness of the soft clay layer in this zone

is typically thicker than 10 m.

BANGKOK GRAOUND STRUCTURE

Shear wave velocity profile of Bangkok basin had been

studied by many researchers. The profile for the first 140

m was proposed by Rabin et al. (2004) based on p-s

logging at 8 locations namely AIT, Thammasart Univer-

sity Rangsit (TUR), Chulalongkorn University (CU), King

Mongkut Institute of Technology Ladkrabang (KMITL),

Nakhon Pathom, Chatuchak, Samut Sakhon, and Ban Tamru.

Teachavorasinskun & Lukkunaprasit (2004) proposed an

equation to determine the velocity of shear wave within the

first 50 m based on soil boring data and p-s logging at three

sites (AIT, CU, and KMITL). A geological section across

200 km along North-South of Bangkok was also proposed by



Depth(m) Vs(m/s) Vp(m/s) ρ(t/m2)

0m - 7m 60 300 1.70

7m - 15m 80 300 1.70

15m - 30m 290 800 1.85

30m - 60m 350 800 1.90

60m - 120m 410 900 1.90

120m - 240m 550 1100 2.00

240m - 720m 720 1500 2.10

720m - 2000 3800 2.35

Table 1 Model of soil deposits for response calculations

Arai & Yamazaki (2003) based on data at AIT. In their model,

the depth of engineering bedrock was shallowest on the north

and increased when moving to the south (coast line). The

deepest bedrock was proposed to be at 550 m below ground

surface. Based on previous studies, a model of soil deposits

as shown in Table 1 was assumed for further analyses. The

first 15 m layers were corresponding to the soft Bangkok clay

layer. The earthquake bedrock was assumed at 720 m below

ground surface.

By using equations (2-3), Young modulus (E) can be

calculated from the following equation:

AMPLIFICATION OF PROPAGATING SHEAR

WAVES

The model in table 1 with a uniform damping ratio of 5%

was used as an input for a 1-D earthquake response analysis

program, SHAKE (Schnabel et al., 1975). The program was

used to determine the magnitude of horizontal ground motion

at ground surface when the incident wave was injected from

the base rock. The ratio between the magnitude of output

motion and input motion at particular frequency is called

Transfer function, T (ω) which can be defined as

T (ω) =E(ω) + F (ω)

2Eo(ω)(2)

where E(ω), F (ω), E0(ω) are incident wave and re-

flected wave at ground surface and incident wave at base

rock, respectively.

In Figure 7, the transfer function of the analysis model

is shown together with measured H/V spectra at station 8

and 67. From the graphs, it can be seen that the peaks at

around 0.7-0.9 sec and at 4 sec can be reproduced from the

simulation. When considering shapes of modal displacement

functions at 0.8 sec and 4 sec periods as shown in Figure 8, it

can be deduced that the long period mode was related across

thick layers to the base rock while the short period mode was

largely involved with the soft clay layer near to the ground

surface.

Figure 7 Calculated transfer function and measured H/V spectra

Figure 8 Displacement profiles of 0.8 sec and 4 sec period modes

H/V SPECTRUM OF RAYLEIGH WAVES

Although microtremor can be viewed as propagated shear

waves from base rock, analyses of microtremor measured

by sensor arrays also showed dispersion characteristics of

Rayleigh waves. Therefore, the H/V spectrum may also be

explained from the theory of Rayleigh waves’ propagation.

Using assumed model (Table 1), H/V spectra of Rayleigh

wave were calculated by Lysmer’s method (Lysmer & Drake,

1973) (Morio et al., 2005) and shown in Figure 9. In this

figure, H/V ratio was calculated by summing the contribution

from 4 modes using the following equation;

H

V=

√

√

√

√

3∑

n=0

R2Hi

√

√

√

√

3∑

n=0

R2V i

(3)

where RHi, RVi are amplitudes of horizontal and vertical


Morio et al.

Figure 9 Calculated H/V spectra of Rayleigh wave, Transfer

Function of SH wave and Observed H/V spectrum

Figure 10 H/V spectra of Rayleigh wave and the participation

factor of each mode Ri

motion of the ith mode.

Based on Figure 9, it can be seen that the peaks at around

0.7-0.9 sec and at 4 sec can also be reproduced from the

theory of Rayleigh waves’ propagation. Figure 10 represents

the same H/V spectrum of Rayleigh wave shown in figure 9

by using logarithm vertical axis. This figure also shows the

participation factors of each mode defined by the following

equation.

Ri =|RHi|

√

√

√

√

3∑

n=0

R2V i

(4)

For the period longer than 0.4 sec, the H/V spectrum is

primarily associated with the fundamental mode. However,

the 1st mode becomes the dominant response mode when the

period is lower than 0.4 sec.

Figure 11 Displacement profiles of fundamental modes of

Rayleigh waves

Figure 12 Variation of H/V spectrum by the thickness of the soft

clay layer (Rayleigh wave propagation)

Displacement profiles of fundamental modes of

Rayleigh waves at 0.8 s and 4 s periods as well as Rayleigh

waves’ phase velocities and their H/V ratio are shown in the

Figure 11. The positive and negative H/V ratios are used for

retrograde and prograde particle motions of Rayleigh waves,

respectively. For the 0.8 s period motion, the displacement

profile was largely related to the top soil deposits in same

manner as that of Figure 8. However, the profile of 4 s period

motion extended over a thickness of 8,000 m, or equal to its

wavelength.

PARAMETRIC STUDIES

To investigate further on the influence of deep ground layer

and the soft clay layer near to the ground surface, two

parametric studies were carried out. The first study varied

the thickness of the soft clay from 15 m to 7 m and 20



Figure 15 Variation of transfer function by the depth of the base

rock (Shear wave propagation)

Figure 13 Variation of transfer function by the thickness of the soft

clay layer (Shear wave propagation)

Figure 14 Variation of H/V spectrum by the depth of the base rock

(Rayleigh wave propagation)

m. The synthesized H/V spectra from Rayleigh and shear

waves propagation models are shown in Figure 12 and 13.

Although, the spectra in Figure 13 is rather noisy the contri-

bution of higher vibration modes, it can be seen from both

figures that the period of peaks increases as the thickness of

the soft clay increases. The second study was conducted by

varying the elevation of base rock from 720 m to 500 m and

1000 m from the ground surface. Based on the synthesized

H/V spectra in Figure 14 and 15, it can be seen that the depth

of base rock affects the long period peaks between 3 and 5

sec. The period of peaks increases as the depth of the base

rock increases.

CONCLUSION

Microtremors were observed at 89 locations in Bangkok and

vicinity area and reported in this study. Two distinct peaks

were found in their H/V spectra. The first peaks ranged

between 0.7 and 0.9 second and the second peaks ranged

from 3 to 6 second. A model of soil deposits was assumed

based on literature data and used in two different approaches,

namely shear wave propagation approach and Rayleigh wave

propagation approach, for the synthesis of H/V spectrum.

Both of them were able to reproduce the predominant periods

at around 0.7-0.9 sec and 3-6 sec. From the analyses, the

short period peaks deemed related to the Bangkok’s soft clay

layer near to the ground surface while the long period peaks

was thought as the response of the whole deposits.

REFERENCES

Arai, H. & Yamazaki, F., 2003. Estimation of S-wave velocity pro-

file using microtremor arrays in the Greater Bangkok, Thailand,

38th annual conference of JGS, pp. 2089–2090.

Kitazumi, A., 2005. Geological feature and earthquake activities in

Thailand, Japanese chamber of commerce, Bangkok, pp. 10–15.

Lysmer, J. & Drake, J. A., 1973. A finite element method for

seismology method in seismological physics, Academic Press.

Morio, S., Kato, Y., & Teachavorasinskun, S., 2005. Complex

eigen-value analyses of Love and Rayleigh waves, 2nd Interna-

tional Symposium on Environmental Vibrations, pp. 11–16.

Morio, S., Kato, Y., Kitazumi, A., Teachavorasinskun, S., &

Charusiri, P., 2007. On the dynamic response of Bangkok

soil layers during strong earthquake considering the effect of

Three Pagoda fault, international symposium on geotechnical

engineering, ground improvement and geosynthetics for human

security and environmental reservation, pp. 349–366.

Nakamura, Y., 1989. A method for dynamic characteristics esti-

mation of subsurface using microtremor on the ground surface,

Quarterly Report of Railway Technical Research Institute, 30(1),

25–33.


Morio et al.

Schnabel, P. B., Lysmer, J., & Seed, H. B., 1975. SHAKE a com-

puter program for earthquake response analysis of horizontally

layered sites, Report No. EERC75-30, University of California,

Berkeley.

Teachavorasinskun, S. & Lukkunaprasit, P., 2004. A simple

correlation for shear wave velocity of soft Bangkok clays,

Geotechnique, 54, 323–326.

Tuladhar, R., Yamazaki, F., Warnitchai, P., & Saita, J., 2004.

Seismic microzonation of the greater Bangkok area using micro-

tremor observations, Earthquake Engineering and Structural

Dynamics, 33, 211–225.

Yordkayhun, S., 2011. Detecting near surface objects using

seismic traveltime tomography: Experimentation at a test site,

Songklanakarin Journal of Science and Technology, 33, 477–

485.


Inversion of Magnetic Data from Remanent and InducedSources

Robert Ellisa,∗, Barry de Wetb, Ian Macleoda

a Geosoft Inc. Suite 810, 207 Queens Quay West, Toronto, ON, Canadab Ivanhoe Australia Ltd. Level 13, 484 St Kilda Road Melbourne, VIC, 3004, Australia


ABSTRACT

Magnetic field data are of fundamental importance in many areas of geophysical exploration with 3D voxel inversion being a common aid

to their interpretation. In the majority of voxel based inversions it is assumed that the magnetic response arises entirely from magnetic

induction. However, in the last decade, several studies have found that remanent magnetization is far more prevalent than previously

thought. Our experience with numerous minerals exploration projects confirms that the presence of non-induced magnetization is the rule

rather than the exception in base metals exploration. In this work we show that failure to accommodate for remanent magnetization in 3D

voxel-based inversion can lead to misleading interpretations. We present a technique we call Magnetization Vector Inversion (MVI), which

incorporates both remanent and induced magnetization without prior knowledge of the direction or strength of remanent magnetization. We

demonstrate our inversion using model studies and field data. Successful application to numerous minerals exploration surveys confirms

that incorporating remanent magnetization is essential for the correct interpretation of magnetic field data.

INTRODUCTION

The utility of magnetic field data in many areas of geophys-

ical exploration is well-known as is the application of 3D

voxel inversion to aid in magnetic data interpretation (for

example, Li and Oldenburg 1996, Pilkington, M., 1997, Silva

et al. 2000, Zhdanov and Portniaguine 2002, to cite just a

few). In the majority of voxel based inversions it is assumed

that the magnetic response arises entirely from magnetic

induction.

However, in the last decade, studies have found that

remanent magnetization is far more prevalent than previously

thought (McEnroe et al. 2009) and affects crustal rocks as

well as zones of mineralization. Unfortunately, remanent

magnetization can seriously distort inversion based on the

assumption that the source is only induced magnetization.

The severity of the distortion is due to the highly non-unique

nature of potential field inversion making it extraordinarily

easy for a potential field inversion to produce a seemingly

plausible model which agrees satisfactorily with the observed

data, even when a fundamental assumption in the inversion is

flawed.

Several authors have reported progress toward magnetic

data inversions including remanent effects (for example,

Shearer and Li 2004, Kubota and Uchiyama 2005, Lelievre

and Oldenburg 2009). In this work we report further progress

in this direction with a technique we call Magnetization

Vector Inversion (MVI), which incorporates both remanent

and induced magnetization without prior knowledge of the

direction or strength of remanent magnetization. In the fol-

lowing sections, we extend conventional scalar susceptibility

inversion to a magnetization vector inversion, that is, we

allow the inversion to solve for the source magnetization

amplitude and direction. While this increases the number

of variables in the inversion we will show by example that

the same regularization principles that allow compact targets

to be resolved in highly unconstrained scalar susceptibility

inversion also apply in vector inversion.

Perhaps our most significant finding is that MVI, or more

generally, inversion including all forms of magnetization,

significantly improves the interpretation of the majority of

minerals based magnetic field inversions. Unfortunately, the

surprising degree of improvement in interpretability cannot

be adequately presented in a paper and can only be verified

by direct experience. Consequently, while we have applied

MVI to a large number of magnetic field surveys and find

the results to be significantly superior to conventional scalar

based inversion, in this paper we are forced to limit our

attention to a synthetic case and field data from the Cu-Au

Osborne deposit located approximately 195km SE of Mount

Isa, in Western Queensland, Australia.


Magnetic inversion from remanent and induced sources

MATHOD AND RESULTS

Let us begin with the very general assumption that the

magnetic properties of the earth can be represented by a

volume magnetization, M(r) (Telford et al. 1990). We make

no assumptions about whether source of the magnetization is

induced, remanent, or otherwise.

From magnetostatics, the magnetic field b at point rjresulting from a volume V containing magnetization, M(r),is given by

B(ri) = ∇∫

v

M)r) · ∇ 1

|r− rj|dr3 (1)

This expression shows directly that the magnetization

vector M(r) is the natural parameter for inversion. This is

a crucial observation.

If the volume V consists of a collection of N sub-

volumes vk each of constant magnetization mk then

Bβ(rj) =

N,3∑

k,α

mk,α

∫

vk

∂α∂β1

|r− rj|dr3 (2)

This defines the forward problem: given a set of sources

mk(k = 1, ..., N) then Bj is the predicted magnetic field

anomaly at points, rj(j = 1, ...,M). Note that the coordinate

index α is summed over indicating that we are free to choose

the most computationally convenient internal coordinate sys-

tem. It also suggests that a coordinate invariant quantity, such

as the amplitude, M(r) = |M(r)|, will be most robustly

determined from the data.

For conciseness, we will represent Eq (2) simply as

B = Gm (3)

The vector magnetization inverse problem is defined as

solving for m given B subject to an appropriate regular-

ization condition. Although there are many choices for the

regularization (see for example, Zhdanov 2002), we choose

without loss of generality, the familiar Tikohonov minimum

gradient regularizer. The inverse problem becomes solving

for m in,

minφ(m) = φD(m) + λφM (m)

φD(m) =M∑

i

∣

∣

∣

∣

Gjm−Bj

ej

∣

∣

∣

∣

2

φM (m) =3∑

γ

|wγ∂γm|2 + |w0m|2

λ : φD(m) = χ2T

(4)

where in the first line, the total objective function φ is the sum

of a data term φD and a model term φM with a Tikohonov

regularization parameter, λ. The second line defines the data

Figure 1 The buried prism model with magnetization vector

orientation (Easterly) shown by the green cones. Side=100m

objective function in terms of the data equation (3) and the

error associated with each data point ej . The third line gives

the model objective function in terms of the gradient of the

model ∂γm and the amplitude of the model, with weighting

terms as required, wγ , w0. The fourth line indicates that the

Tikhonov regularization parameter λ is chosen based on a

satisfactory fit to the data in a chi-squared sense, χ2T . In

addition, other constraints, such as upper and lower bounds,

can be placed on m as appropriate to the specific exploration

problem.

Example - Buried Prism

Although the buried prism model is far too simplistic to

have exploration significance, it does make an excellent

pedagogical example, so we follow tradition and begin by

considering the inversion of simulated TMI data over a buried

prism with magnetization vector M perpendicular to the

earth field. The model consists a cube with side length 40

m buried with a depth to top of 20 m and a magnetization

vector in the EW direction, (My = 0, Mz = 0) as shown in

Figure 1.

Simulated TMI data are shown in Figure 2 for Earth

field with inclination 90° and amplitude 24000 nT. Cardinal

directions have been chosen only for simplicity of explana-

tion; any directions could be chosen with equivalent results.

Also for simplicity, the data were simulated at 20 m constant

clearance and on a regular 8m grid.

Inverting the TMI data in Figure 2 yields the model

shown in Figure 3 which should be compared to the true

model shown in Figure 1. There is some variability in

the magnetization direction but the predominant direction is

clearly EW, in agreement with the true model.

Vector magnetization models in 3D are difficult to inter-

pret directly in all the but the simplest cases. In real-world


Ellis et al.

Figure 2 The TMI data simulated over the magnetization vector

model shown in Figure 1. The axes are in metres.

Figure 3 The MVI recovered model for comparison with Figure 1.

The magnetization vector orientation is shown by the green cones.

exploration we need some simpler derived scalars which

highlight the important information in the vector model. As

suggested by Eq (1), the most robust and meaningful scalar is

the amplitude of the vector magnetization and this should be

the primary quantity used in interpretation. However, since

the magnetization vector direction is the earth field direction

for induced sources, it is tempting to attempt to use the

directional information recovered in MVI to generate scalars

related to the earth field direction.

There are many possibilities but we have found that three

useful derived scalars for exploration are: the amplitude of

the magnetization, the earth field projection of the magneti-

zation, and the amplitude of the perpendicular-to-earth-field

components of the magnetization. In exploration problems,

the amplitude is robust by being independent on of any

assumptions regarding the earth field, while the amplitude

Figure 4 (a) A cross section through the true model, (b) the recov-

ered amplitude of the magnetization vector, (c) the amplitude of the

perpendicular-to-earth-field components of the magnetization, (d)

the projection of the magnetization on to the earth field direction.

The colour scales indicate the MVI magnetization in normalized to

SI (see text).

perpendicular is an approximate indicator of non-induced

magnetization. To support our findings, these three derived

scalars are shown in Figure 4b, c, d for an East-West slice

through the model volume bisecting the target in the true

model.

In exploration situations it is convenient to present MVI

output M normalized by the amplitude of the earth’s mag-

netic intensity in the area of interest. That is, our results

are displayed as M/HE where HE is the amplitude of

the earth’s magnetic intensity in the area of interest. By

using this normalization in an area of purely induced mag-

netization, the numerical values returned by MVI inversion

will be directly comparable to those of scalar susceptibility

inversion, in our case in SI.

For completeness, and to show the contrast between

MVI and conventional scalar inversion, Figure 5b shows the

equivalent section through a model produced by an inversion

which assumes only induced magnetization. As should be

expected, the recovered model using scalar inversion is a

very poor representation of the true model, which in real-

world exploration ultimately adds significant confusion to the

interpretation process.

This simple prism example demonstrates the power of

magnetization vector inversion and its advantage over scalar

susceptibility inversion in cases where the magnetization

vector direction deviates from the earth field direction. We

argue that this situation predominates in real-world explo-

ration environments based on experience from many mag-

netic surveys, however this cannot be shown here.


Magnetic inversion from remanent and induced sources

Figure 5 (a) A cross section through the true model, (b) the re-

covered scalar susceptibility. The color bar shows the susceptibility

magnitude in SI.

Example - Osborne

The preceding pedagogical study of MVI on simulated data

over a prism provides a solid basis for the much more

important application of MVI to field data. As mentioned

in the Introduction, it is hard to appreciate fully the impact

on magnetic data interpretation by including non-induced

magnetic sources. However, to motivate our assertion, we

present typical results taken from TMI data collected over

the Osborne deposit.

The history of the Osborne mine is well described

elsewhere, see for example, Rutherford et al. 2005. Briefly,

significant Cu-Au mineralization beneath 30-50m of deeply

weathered cover was confirmed in 1989. Intense drilling

between 1990 and 1993 defined a total measured and in-

dicated resource of 11.2 Mt at 3.51% Cu and 1.49 g/t Au.

Exploration since 1995 has delineated high-grade primary

mineralization dipping steeply East to some 1100 m vertical

depth. As of 2001, total mined, un-mined and indicated

resources are reported to be about 36 Mt and 1.1%Cu and

1 g/t Au (Tullemans et al. 2001). Current exploration is

focused on mapping the high- grade mineralization to greater

depths and mapping similar structures in the surrounding

area. The geophysics includes total magnetic intensity (TMI)

data over the property, which is shown in Figure 6. The TMI

data were acquired in 1997 flown at 40 m clearance on 40 m

line spacing.

Magnetization Vector Inversion of the Osborne TMI data

yields the magnetization vector amplitude earth model shown

in Figure 7. Superimposed (in black) is the subsequently

discovered mineralization from extensive drilling and un-

derground mining. For comparison, Figure 8 shows the

corresponding scalar susceptibility inversion. Comparing

Figure 7 and Figure 8 shows that inverting for the magne-

tization vector provides a much better model for interpreta-

tion. The scalar inversion fails to represent reality in this

case suggesting, most likely, that the scalar assumption is

violated: a common occurrence in mineral exploration in

our experience. In contrast the MVI model is consistent

with the drilling results, and furthermore, indicates a steeply

dipping volume on the Eastern flank. The strong near surface

anomaly to the west of the dipping zone is known banded

Figure 6 The observed TMI data acquired over the Osborne

property. The axes are in metres. The color scale shows the TMI

amplitude in nT.

Figure 7 An EW section through the recovered MVI model am-

plitude at the Osborne property with the now known mineralization

shown in black. The color bar gives the normalized amplitude in SI.

The axes are in metres.

ironstone.

CONCLUSION

We have argued that remanent magnetization must be in-

cluded in magnetic field data inversion in order to avoid

seriously misleading interpretations. To support this argu-

ment we demonstrated the value of Magnetization Vector

Inversion using model studies, and field data from the Os-

borne property. The degree of improvement afforded by

using MVI in all areas of magnetic field data inversion may

seem surprising, however recent advances in understanding

remanent magnetism suggest that non-induced magnetiza-

tion plays a far more important role than previously thought

in the origin of magnetic anomalies. Successful application

to numerous minerals exploration surveys confirms that in-


Ellis et al.

Figure 8 The same section as in Figure 7 for the scalar model

with drilling and mineralization in black. The color bar gives the

susceptibility in SI. The axes are in metres.

corporating remanent magnetization is recommended for the

correct interpretation of the majority of magnetic field data.

REFERENCES

Butler, R. F., 1992. Paleomagnetism: magnetic domains to geologic

terranes, Blackwell Scientific Publications.

Geophysics, A., 1990. Telford, W. M. and Geldart, L. P. and

Sherriff, R. E. and Keys, D. A., Cambridge University Press.

Kubota, R. & Uchiyama, A., 2005. Three-dimensional magneti-

zation vector inversion of a seamount, Earth Planets Space, 57,

691–699.

Lelievre, P. G. & Oldenburg, D. W., 2009. A 3D total magnetization

inversion applicable when significant complicated remanence is

present, Geophysics, 74, 21–30.

Li, Y. & Oldenburg, D. W., 1996. 3-D inversion of magnetic data,

Geophysics, 61, 394–408.

McEnroe, S. A., Fabian, K., Robinson, P., Gaina, C., & Brown, L.,

2009. Crustal magnetism, Lamellar magnetism and rocks that

remember, Elements, 5, Elements.

Pilkington, M., 1997. 3-D magnetic imaging using conjugate

gradients, Geophysics, 62, 1132–1142.

Rutherford, N. F., Lawrance, L. M., & Sparks, G., 2005. Osborne

cu-au deposit, clonclurry, north west queensland, Tech. rep.,

CRC LEME Report.

Shearer, S., , & Li, Y., 2004. 3D inversion of magnetic total gradient

data in the presence of remanent magnetization, in 74th Annual

Meeting, SEG, Technical Program, Expanded Abstracts.

Silva, J. B. C., Medeiros, W. E., & Barbosa, V. C. F., 2001.

Potential-field inversion: Choosing the appropriate technique to

solve a geologic problem, Geophysics, 66, 511–520.

Tullemans, F. J., P., A., & Voulgaris, P., 2001. The role of geology

and exploration within the mining cycle at the Osborne mine,

NW Queensland, in monograph 23 - mineral resource and ore

reserve estimation - the AusIMM guide to good practice, Tech.

rep., Australian Institute of Mining and Metallurgy.

Zhdanov, M. S., 2002. Geophysical inverse theory and regulariza-tion problems, in Method in Geochemistry and Geophysics 36,

Elsevier Science.

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with data compression and image focusing, Geophysics, 67,

1532–1541.


Extracting shear wave velocity from seismic reflectiondata: Case studies in near surface characterization usingMultichannel Analysis of Surface Wave (MASW)

Sawasdee Yordkayhuna,b,∗, Aksara Mayamaea,, Preeya Srisuwana,

a Department of Physics, Faculty of Science, Prince of Songkla University, Hat Yai ,90112, THAILANDb Geophysics Research Center, Department of Physics, Faculty of Science, Prince of Songkla University, Hat Yai ,90112,


ABSTRACT

Understanding elastic properties of near-surface material are importance in geotechnical, earthquake engineering and environmental studies.

Using compressional (P) wave seismic reflection for detecting shallow buried objects is difficult when the data are lack of high frequency

contents and the near surface is of highly heterogeneity. In addition, the difficulty in data processing may be due to the presence of P-waves

interfering with noises of shear (S) wave and surface wave. For seismic refraction, it is based on the assumption of velocity increase with

depth whereas velocity inversions in the real earth layers can lead to the pitfalls in the interpretation. To show that near surface layers

can be characterized by shear wave velocity obtained from seismic reflection data, some case studies were demonstrated. By applying

Multichannel Analysis of Surface Wave (MASW) technique, we take advantage of surface wave from the three seismic dataset, testing

across a buried drainpipe area, a sinkhole area and a fault detection area. MASW is implemented as an iterative inversion technique for

reconstructing shear wave velocity model from dispersion curves of the surface wave. In the buried drainpipe area and the fault detection

area, although the location of targets was not clearly resolved, the evidence of lateral and vertical velocity variation has potential to evaluate

the soil stress changing due to the disturbed ground and increased load. In the sinkhole area, a suspected void was observed as indicated

by the anomaly pattern of a low velocity feature underlying a high velocity due to the induced stress on the wall and roof of cavern. The

results revealed that elastic properties of the shallow subsurface can be obtained from joint interpretation of P-wave and S-wave, without

significant increase in acquisition and testing time.

KEYWORDS: surface wave, shear wave, MASW, near-surface object, seismic reflection

INTRODUCTION

Understanding elastic properties of the near surface mate-

rial, especially shear (S) wave velocity (Vs) are importance

in geotechnical, earthquake engineering and environmental

studies. Generally, the determinations of S-wave data can

be done either in direct way (using downhole or cross-

hole and surface seismic methods) or indirect ways (using

empirical relation with N value from Standard Penetration

Test, SPT). In case of downhole or crosshole (e.g., VSP),

the measurements are expensive because several boreholes

need to be drilled, and it is difficult to conduct in urban

areas. For surface seismic methods, compressional (P) and

S-wave reflection/refraction data is considered to be standard

technique for P and S-wave velocity determination. How-

ever, it is generally accepted that refraction methods cannot

handle velocity inversions and hidden layers problems. For

deep investigations, long profile is necessary which make it

difficult to run measurements in urban areas. In addition,

the pitfalls in data processing might be due to the presence

of P-waves interfering with S-wave arrivals. For indirect

way of shear wave velocity determination, several empirical

relationships exist for different lithology and tend to be site

dependent (Akin et al., 2011).

In recent years, a new technique for shear wave veloc-

ity determination, Multichannel Analysis of Surface Waves

(MASW), was developed and increasingly used in earth-

quake and geotechnical engineering because it is non-

intrusive, fast and cost-effectively geophysical method (Park

et al., 1999). Surface waves (e.g., Rayleigh wave or ground

roll) are typically considered as noise for seismic reflection

and refraction surveys. On the other hand, it becomes

signal and contain useful information in the MASW method.

The advantages of analyzing surface wave are: 1) it is


Multichannel analysis of surface wave

coherent event in the shot records because more than two-

thirds of total seismic energy generated is contributed into

Rayleigh waves (Richart et al., 1970). 2) Even in the case of

velocity inversion, Rayleigh wave dispersive characteristics

can be used to estimate S-wave velocities of the near-surface

(Doyle, 1995; Xia et al., 2002).

Numerous studies have shown that S-wave velocity as

derived from MASW method can be used in a range of

applications. For example, estimating the amplification of

earthquake-induced ground motion for quantitative earth-

quake hazard assessment and site response studies (Ergina

et al., 2004; Anbazhagan & Sitharam, 2008; Maheswari et

al., 2010), Liquefaction potential analysis (Kayabali, 1996;

Andrus & Stokoe, 2000; Karastathis et al., 2010), the control

of soil compaction (Kanli et al., 2006), the detection of

underground cavities, tunnels and sinkholes (Leparoux et al.,

2000).

The purpose of this study is to determine S-wave velocity

at the sites by using MASW method with regard to a wide

range of applications. Although the data were recorded for

reflection and tomography analysis (Mayamae & Durrast,

2010; Yordkayhun, 2011), here we take advantage of using

the same dataset for MASW method based on the fact

that the data are contaminated by strong ground roll and

our natural frequency of geophone is slightly low (14 Hz).

Interpretation of S-wave velocity results attempt to test the

potential of the method in detecting near-surface velocity

anomalies associated with the known target location.

THEORETICAL BACKGROUND OF SURFACE

WAVE METHODS

Rayleigh and S-wave velocities

Rayleigh wave (or ground roll) is a surface wave which

its particle motion is a combination of an elliptical and

retrograde motion and its amplitude decay is exponentially

with depth from the free surface (Lin et al., 2007). In seismic

survey, it is majority part of seismic energy propagating

which can be characterized by strong amplitude and low

frequencies. In case of inhomogeneous media, it is disper-

sive, different frequencies travel at the different velocities

(velocity is dependent on frequency) with multiple modes.

Dispersion characteristic of Rayleigh waves is the crucial

property to estimate the S-wave velocities for the MASW

method.

In an elastic half space, relationship between the S-waves

and Rayleigh wave velocities is expressed as follows (Richart

el al., 1970).(

VR

VS

)6

+ 8

(

VR

VS

)4

+

(

24− 161− 2σ

2− 2σ

)(

VR

VS

)2

+16

(

1− 2σ

2− 2σ− 1

)

= 0

(1)

Where VR is Rayleigh wave velocity and σ is Poisson

ratio. Normally, Poisson’s ratio range from 0 to 0.5 for

very stiff solids to fluids (Sheriff, 1991). Therefore, the

Rayleigh wave velocity ranges from 0.87 to 0.96 of S-wave

velocity. As mentioned earlier and based on this equation,

the Rayleigh wave phase velocity of a layered earth is a

function of frequency and subsurface properties including

P-wave velocity, S-wave velocity, density, and thickness of

layers.

In geotechnical engineering, the dynamic elastic proper-

ties of soil are importance for site investigation and construc-

tion purposes. These properties can be derived from seismic

velocities and density. In an elastic medium, the propagation

velocity of S-waves is given by (Dobrin & Savit, 1988):

VS =

√

µ

ρ(2)

Where µ is the shear modulus and ρ is the density. Based on

this relationship, shear modulus can be calculated. Given the

relationship between P and S wave velocity, Poisson’s ratio

can be derived from equation:

σ =V 2P − 2V 2

S

2(V 2P − V 2

S )(3)

By using equations (2-3), Young modulus (E) can be calcu-

lated from the following equation:

V 2P =

E

ρ

1− σ

(1 + σ)(1− 2σ)(4)

MASW METHOD

The MASW technique relies on the principles of the dis-

persion analysis and inverse theory (Menke, 1989). The

method of determining subsurface S-wave velocities consists

of following steps:

(i) Data acquisition. Data are recorded in the same way as

the conventional seismic reflection/refraction acquisition

except the low natural frequency geophones (∼4.5 Hz)

are typically used (Figure 1a). Some rules of thumb for

optimum data acquisition are given by Xia et al. (1999).

(ii) Dispersion analysis and pick. Dispersion energy is

constructed using the 2D transformation discussed by

Park et al. (1998) to maps a shot gather in time-

space (t-x) domain into the phase velocity-frequency (f-

v) domain (Figure 1b). Then dispersion curve can be

extracted by picking the peaks of dispersion energy over

different frequency values.

(iii) Dispersion curves inversion. Inversion of dispersion

curve is non-linear and has non-unique solutions. The

iterative least-squares inverse routine is a standard tech-

nique for dealing with this matter. By setting up a suit-

able initial model and adjusting the model parameter val-

ues (the S-wave velocity) with the object of minimizing

the error between the calculated and picked dispersion

curve, a 1D velocity profile is obtained (Figure 1b).


Yordkayhun et al.

Figure 1 Data acquisition (a) and analysis of MASW methods (b).

(iv) Generating 2D S-wave velocity sections. The result

of dispersion curve inversion is a 1D S-wave velocity-

depth model, locating at the middle of the geophone

spread. To generate a 2D S-wave velocity section, the

data should be collected in a CMP roll-along or the fixed

spread acquisition manner. The velocity contours are

obtained by the gridding and interpolating the velocity

model along the survey line or by performing the CMP

crosscorrelation (Hayashi & Suzuki, 2004).

CASE STUDIES

We present three cases studies to demonstrate the possibili-

ties of the MASW technique based on the seismic reflection

data. Field parameters are summarized in Table 1.

Case I: A buried drainpipe area

Site and data description

A seismic reflection test line is oriented perpendicular to 6

drainpipes on flat ground surface topography (Figure 2a).

Each concrete drainpipe has a diameter of 1 m, buried at

about 2 m depth in highly compacted subsurface underlying

sand and gravel overburden. The data were acquired by using

acquisition parameters as summarized in Table 1.

As mentioned before, the recorded data was initially

designed for conducting a seismic reflection experiment

across buried objects at a test site. Fortunately, no prepro-

cessing or filtering had been applied during data recording

and our natural frequency geophone is quite low (14 Hz).

Consequently, the Rayleigh wave is clearly seen in the shot

gather which are characterized by the strong amplitude and

linear event with low apparent phase velocity (Figure 3a).

Portions of data as marked by dashed rectangle in Figure 2b

were used for this study.

The data were interpreted using SeisImager/SW (Geo-

metrics Inc.) dispersion-inversion software. A shot gather at

the beginning (forward shot) and at the end of profile (reverse

Table 1 Acquisition parameters and equipment.

Parameter Details

Case I Case II Case III

Energy sources 10kg

sledgeham-

mer

10kg

sledgeham-

mer

10kg

sledgeham-

mer

Shot spacing 2 m 2 m 5 m

Geophone

frequency

14 Hz 14 Hz 14 Hz

Geophone spac-

ing

1 m 2 m 4 m

Offset Min/Max 1/25 m 30/52 m 24/116 m

Field geometry Fixed

spread

Roll along Roll along

Recording

system

Geometric

SmartSeis

Geometric

SmartSeis

Geometric

SmartSeis

No. of channels 24 channels 12 channels 24 channels

Record length 500 ms 350 ms 1000 ms

Sampling

interval

0.25 ms 0.25 ms 0.5 ms

Figure 2 Surface topography and drainpipe series in the test site

(a) and profile geometry (b). Dashed area highlight the area used

for MASW analysis (modified from Yordkayhun, 2011).

shot) with their dispersion curves are illustrated in Figure 3b.

For more accurate dispersion picking, quality control of the

picks is done by manually refined by visual inspection. Note

that the dispersion characteristic of the two shots is somewhat

difference (Figure 3b). In particular, the larger uncertainty is

observed at the higher frequencies for the reverse shot where

the higher mode of dispersion exists. This may indicate a

strong velocity variation in the test site. For more accurate

inversion, the initial model is set based on P-wave velocity



Figure 3 Example of raw shot gathers (a), Dispersion curves (b)

and final shear wave velocity models (c).

information from seismic tomography (Yordkayhun, 2011)

and characterizing apparent phase velocities of about 500-

700 m/s in the shot records. The inversion was run over

five iterations, and root mean square (RMS) data error was

tracked to obtain a minimum structure model and test the

convergence to the final solution.

Results and discussions

The model convergence and stability were evaluated based

on RMS data error. Tracking the RMS data error during the

inverse process has shown that model stability on the solution

occurred after the 4th iteration (Figure 3c) and yields the final

RMS error of about 6%. Using the same initial model for

Figure 4 Dispersion curves (a) and RMS error of each iteration

of inversion (b). Comparison of tomography (c) and shear wave

velocity cross sections (d).

the inversion, the difference in model convergence between

the forward and reverse shot is very small, implying that the

inversion is relatively stable.

Figure 4d shows the 2D S-wave velocity section pre-

sented as distribution of seismic velocity along the profile

together with their dispersion curves for each CMP. In

general, the section reveals S-wave velocity in the range of

200-500 m/s above 10 m depth of subsurface. Although there

is general trend of velocity increase with depth, the local

velocity inversion is observed in the section. This may reflect

the variety of ground compaction at the upper 2 m depth.

Comparison of the dispersion curves along the test profile

support this observation (Figure 4a).

Correlation with tomography results

In Figure 4, the S-wave velocity section corresponding to

depths less than 10 m were compared with P-wave tomog-

raphy section. Note that the length of S-wave section is not

as long as the tomography section due to the limitation of

MASW geometry.

In tomography section (Figure 4c), the low velocity layer

with the thickness of 1-2 m may correspond to unconsoli-


Yordkayhun et al.

dated sediments cover of sand and gravel. The underlying

high velocity layer is interpreted to be highly compacted rock

fragments and gravel. The drainpipe series is correlated well

with the low velocity zone in the middle of the tomographic

image. Although the drainpipe position is not clearly de-

tected in the S-wave velocity section, there is evidence of

effect of the elastic properties changing around drainpipe

where the low velocity anomaly is observed (S-wave velocity

decreased near the surface and below 5m depth). The

decreased velocity in the overburden and surrounding rock

might be due to stress relief and relaxation after drainpipe

installation.

Case II: A sinkhole area


The study area lies in a village in Nakhon Si Thammarat

Province, where 2 adjacent sinkholes were found in 2005

and 2009, respectively. The sinkholes were at about half

a kilometer away from an active gypsum mine in a village

(Figure 5). The existence of the subsurface evaporate for-

mation is evidences of the sinkhole development associated

with the mining. To understand the mechanism of sinkhole

occurrence, a number of geophysical methods including

resistivity, self potential, seismic refraction and reflections

were applied in this area. The detailed results were described

by Mayamae & Durrast (2010) and here the briefly details

were reported.

Vertical electrical sounding measurements revealed a

weathered anhydrite/gypsum layer (80 ohm-m) at about 15 to

25 m depth. This layer is underlain by a very high resistivity

layer of solid, non-weathered sulfate rocks. Weathering of

the sulfate is mainly at the top and along open joints in the

rock mass. Seismic refraction data revealed the overlying

clay and sandy clay layers with the estimated water table,

whereas seismic reflection data provided information about

the weathered gypsum/anhydrite layer. It was concluded that

the sinkhole in this area was initially developed by forming of

caverns in the subsurface due to an increased dissolution of

the sulfates in the weathered sulfate layer which is in larger

contact with the groundwater layer. When the water in the

caverns decreased, parts of the overlying sediments probably

collapse into the empty caverns, leading to the sinkhole.

For MASW method, we use the seismic reflection data,

performing at about 20 m apart from a 15 m diameter, 10

m depth sinkhole as shown in Figure 5. Data were recorded

using parameters shown in Table 1.


Tracking the RMS data error during the inverse procedure

on this site has shown that model stability on the solution

occurred after the 5th iteration (Figure 6b). The model yields

final RMS error of about 8%.

Figure 5 Sketched map of study area showing geophysical survey

positions. Sinkholes are marked by black dots (modified from

Mayamae & Durrast, 2010).

Figure 6 Dispersion curves (a) and RMS error of each iteration of

inversion (b). Comparison of shear wave velocity cross sections (c),

lithology (d) and stacked section (e). Dashed polygon indicated the

suspected void area.

Figure 6c shows the 2D S-wave velocity section pre-



Figure 7 Study site of fault detection area showing seismic survey

lines (blue line) and suspected faults (red line).

sented as distribution of seismic velocity along the profile

together with their dispersion curves through each CMP

(Figure 6a). The section reveals S-wave velocity in the range

of 200-500 m/s above 20 m depth of subsurface.

The strong vertical and lateral velocity variation is ob-

served in this area as well as the local velocity inversion. The

suspected void is observed as indicated by the anomaly high-

lighted by the dashed polygon. The pattern of a low velocity

feature with a high velocity closure above it can be used

as an indicator of suspected void that have been studied in

the mine working and dissolution features elsewhere (Sloan

et al., 2009). In this area shear wave velocity increased

probably due to the induced stress on the wall and roof of

the void.

CASE III: A FAULT DETECTION AREA


The study area lies in Wiphavadee district, Surat Thani

Province, where the suspected Klong Marui Fault Zone

exists. In conjunction with the ongoing fault investigation

and characterization project, a number of seismic reflection

profiles were applied in this area (Figure 7). The data were

acquired by using acquisition parameters as summarized in

Table 1.


Figure 8 shows comparison of S-wave velocity section and

stacked section of a selected profile. Even though the fault

zone can be identified on the stacked section, there is a lack

of information near the ground surface because acquisition

Figure 8 (a) Shear wave velocity section showing fault zone

(dashed area). (b) Stacked section showing fault position (red lines).

Note that this section is the results of seismic line 2 as indicated in

Figure 7.

geometry and data processing. Additional results from S-

wave velocity can fill this information gap at the upper 20 m

depth. The effect of the shear stress changing can be seen

on the S-wave velocity section (upper 10 m depth) where the

lateral velocity variation is observed. Over the fault zone, the

increased load on the rock matrix may leads to compaction

and increased velocity within the subsurface.

CONCLUSIONS

We have demonstrated that when the surface waves are

dominated in the seismic reflection data, the same dataset

can be used for determining the S-wave velocity of the

near surface by using MASW method. Elastic properties

of near-surface materials, especially S-wave velocity are

important parameter for evaluating the dynamic behaviors of

soil and are applied for earthquake and civil engineering site

investigation.

The case studies show that evidence of lateral velocity

variation has potential to identify the soil induced changes

in the localized stress field due to the disturbed ground

and loading. The method in this study can be considered

as a cost effective, non-invasive tool for environmental

and engineering studies. However, the cases carried out

on this datasets highlight some considerations to be taken

into account, particularly recording data with low frequency

geophone and using the longer record length could improve

the MASW results at the test sites. In addition, lithology in-

formation from borehole or SPT test should be incorporated

for evaluating the MASW performance.

ACKNOWLEDGMENT

We greatly appreciate Department of Physics, Faculty of

Science, PSU and EGAT for their instruments and financial


Yordkayhun et al.

support.

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of shear wave velocity (Vs) and penetration resistance (SPT-N)

for different soils in an earthquake-prone area (Erbaa-Turkey),

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Anbazhagan, P. & Sitharam, T., 2008. Mapping of average shear

wave velocity for Bangalore region: a case study, Journal of

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485.


Quality Improvement Comparison Between Time-SpaceWindow Varying Median Filter and Time Window VaryingMedian Filter

Siriphon Somsria,∗, Pisanu Wongpornchaia,b

a Applied Geophysics Program, Faculty of Science, Chiang Mai Universityb Thailand Center of Excellent in Physics (ThEP), Commission on Higher Education, Bangkok 10400, Thailand


ABSTRACT

Noise in seismic data affects the signal to noise ratio, obscures details, and complicates identification of useful information. The random

and coherent noises in seismic data cannot be avoided during the recording. However, non-linear filters can eliminate these noises. A

non-linear filter is any filter that does not meet the criteria of linearity such as one-dimensional time varying median filter. The principle

of these filters is based on median filter but there were developed to enhance efficiency. These experiments present the abilities of filters

to improve signal of un-stack, deconvolved and stacked noisy seismic data. Signal to noise ratio, the subtracted value between filtered

and non-filter data and the result after applying AGC can be used to compare the filters competence. For three data types, the time-space

varying median filter (TSVMF) can more reduce the random noise, ground roll and refracted wave than time varying median (TVMF).

However, both filters can preserve the signal.

KEYWORDS: Time-space varying median filter, time varying median filter

INTRODUCTION

The seismic method is the most important geophysical tech-

nique in terms of expenditures and number of geophysicists

involved. It is predominantly due to high accuracy, high

resolution, and great penetration. However, the reliability of

seismic method is strongly depended on the quality of data

recording. Noise such as ground roll can drop the data quality

and cannot be avoided in explorations.

Random noise is a non-predictable noise but also certain

statistical properties (Telford et al, 1990). Some of this

seismic random noise exhibits spike-like characteristics and

neither continuous nor correlative (Liu et al, 2009). The

random noise can be produced from many sources such as

wind, tree shaking, and human activities.

Ground roll is the Rayleigh wave but it is often called the

ground roll in seismic exploration. Ground roll is a coherent

noise which propagates at the surface of the earth, at low

frequency and low velocity which are below 10 Hz and 100

to 1000 m/s, respectively (Olhovich, 1964). The ground roll

energy is high so it often obscures reflected seismic data.

(Saatcilar and Canitez, 1988).

Noise eliminating can be achieved in processing steps

by filters. Two of non-linear filters, time window varying

median filter and time-space window varying median filter,

were tested with noisy seismic data. The most useful of these

filters is denoising most of spiky noise or random noise and a

few of ground roll. The abilities of the filters are considered

from keeping signal and denoising.

The time window varying median filter (TVMF) was

presented by Liu et al. (2009). TVMF is one-dimensional

median filter which window size is designed from threshold

value. The threshold value is calculated from the average

of the signal amplitude. The prestack data from Texas was

applied by TVMF. The TVMF can eliminate random noise

enough to enhance the continuity of events. This method is

more powerful to eliminate random noise than the stationary

window median filter.

The two-dimensional median filter was developed for

enhanced efficiency. Vijaykumar et al. (2007) used an

adaptive window length recursive weighted median filter

(ARWMF) in an image processing and they considered the

size of window by using the density of noise

So from this idea, the time-space varying median filter

(TSVMF) was developed. This filter can adapt by using a

threshold value which different from ARWMF. And the filter

is also similar to TVMF but it is a two-dimensional filter.

Filter efficiency is determined by the signal to noise


Time-space and time window varying median filter

Figure 1 Algorithm of TSVMF.

ratio (S/N) and the difference between filtered data and non-

filtered data by their subtraction. Good filters would provide

high S/N value which reduces only the high amplitude noise.

The window of S/N analysis can be represented by win-

dow D (Equation 1). S/N can be calculated using Equation 2

(Liu et al, 2009).

D = [xi,j ] (0 < M 6 Nx, 0 < N 6 Nt) (1)

S/N = 10 log10

N∑

j=1

(

M∑

i=1

xi,j

)2

MN∑

j=1

M∑

i=1

x2

i,j−N∑

j=1

(

M∑

i=1

xi,j

)2

(2)

where xi,j is amplitude signal in the window D, M and Nare size of window D which consist of trace number and sam-

ple number, respectively. Subtraction between filtered and

non-filtered data is an easy checking on noise elimination.

The appearance of interested noises in subtracted value is the

efficiency of filter that can reduce the noises.

Figure 2 a) Synthetic data b) Synthetic data with salt pepper

noise c) Synthetic data after filter by TSVMF d). Subtracted value

between filtered data and non-filtered data.

Developed TSVMF

TSVMF was developed based on TVMF. The size of refer-

ence window should be small such as 5×5 sizes (C1 × C2).

The threshold values (T ) are calculated using Equation 3.

T =1

NxNt

Nx∑

i=1

Nt∑

j=1

|YC1,C2| (3)

where |YC1,C2| is median value from reference window. Nx

is number of samples and Nt is number of traces.


Somsri and Wongpornchai

Figure 3 a) un-stacked data b) TSVMF data c) TVMF data

d) subtracted value between filtered and non-filtered data from

TSVMF and e) subtracted value between filtered and non-filtered

data from TVMF.

The two selected constant values were added to the

reference window (C1, C2) for adjusting the window size in

both time (C1) and space (C2) directions. Then the new

windows are represented by (NewC1, NewC2) (Equations

4 and 5).

NewC1 =

C1 + α1 0 < |YC1,C2| 6 T/2

C1 + β1 T/2 < |YC1,C2| 6 T

C1 + γ1 T < |YC1,C2| 6 T/2

C1 + δ1 |YC1,C2| > 2T

(4)

Figure 4 Comparison between non-filtered data and filtered data

that are applied with AGC, these data are a) Un-stacked data, b)

TSVMF data and c) TVMF data.

NewC2 =

C2 + α2 0 < |YC1,C2| 6 T/2

C2 + β2 T/2 < |YC1,C2| 6 T

C2 + γ2 T < |YC1,C2| 6 T/2

C2 + δ2 |YC1,C2| > 2T

(5)

where α1, β1, γ1, and δ1 are selected constant values for C1.

α2, β2, γ2, and δ2 are selected constant values for C2.

The selected constant values can be defined from exper-

iment. Increasing the window length (in time-direction) can

improve S/N.

In space-directions, the parameter should be the smaller

value because the member in a two-dimensional window will

be too large then the filtered data will be aliasing. If the

median value is judged as a noise the window would be

bigger but if the value is judged as a useful data the window

would be smaller. The width and length of the window

should up to 20 and 60, respectively. The TSVMF algorithm

is shown in Figure 1.

There are three types of data for testing: un-stacked data,

deconvolved data and stacked data. The details of these data

are shown in Table-1. The qualities of the data are very poor

because the signal to noise ratio (S/N) are minus valued. The

window size parameter of TSVMF was tested on un-stacked

data.



Figure 5 a) Deconvolved data b) TSVMF data c) TVMF data

d) subtracted value between filtered and non-filtered data from

TSVMF and e) subtracted value between filtered and non-filtered

data from TVMF.

Data type Trace number Samples S/N

Un-stacked data 44 1999 -38.44

Deconvolved data 44 1999 -41.16

Stacked data 550 1400 -50.65

Table 1 Details of the tested data.

Figure 6 Comparison among non-filtered data and filtered data that

are applied with AGC, these data are a) Un-stacked data, b) TSVMF

data and c) TVMF data.

METHODOLOGY

The source codes of filters in C language were developed

from TSVMF algorithms and were combined with “Mada-

gascar”, open source software package. The TSVMF was

tested on synthetic data. Then the filters were applied

on seismic data before and after deconvolved and stacked

data. The signal to noise ratio and subtracted value were

used to compare quality improvement among conventional

processed data, conventional processed data with TVMF and

conventional processed data with TSVMF.


Synthetic data tested

The synthetic data was created using the elastic Equation.

The ellipse in Figure 2a shows ground roll in shot record.

The salt pepper noise was added to synthetic data as a

random noise in seismic data. In Figure 2c presents a filtered

synthetic data which do not differ from the Figure 2b. The

subtracted value between filtered and non-filtered data is

shown in Figure 2d. The TSVMF can reduce the ground roll

because the ground roll appears in subtracted value. After

applying filters, the signal shows a small change as in a

rectangular in Figure 2d and the salt pepper noise also mixes

with the signal.


Somsri and Wongpornchai

Figure 7 Comparison of the filtered and non-filtered data

Un-stacked, deconvolved and stacked data tested

In Figure 3a, 3b, 3c show the filtered data using TSVMF

(Figure 3b) and TVMF (Figure 3c). The arrows in Figure

3a to 3c are pointing to the amplitude changing for refracted

wave and ground roll. The TSVMF can better reduce the

refracted wave and ground roll more than TVMF as shown

by arrows 1 and 2 in Figure 3a and Figure 3b. Even if, the

useful signals appear in subtracted values for TSVMF, but

after useful data was applied by AGC, it looks similar to non-

filtered data which represent by rectangle area in Figure 4a to

4c. Both filters can preserve the useful data.

In Figure 3a, 3b, 3c show the filtered data using TSVMF

(Figure 3b) and TVMF (Figure 3c). The arrows in Figure

3a to 3c are pointing to the amplitude changing for refracted

wave and ground roll. The TSVMF can better reduce the

refracted wave and ground roll more than TVMF as shown

by arrows 1 and 2 in Figure 3a and Figure 3b. Even if, the

useful signals appear in subtracted values for TSVMF, but

Figure 8 Comparison among the filtered and non-filtered data

which are applied by AGC.

after useful data was applied by AGC, it looks similar to non-

filtered data which represent by rectangle area in Figure 4a to

4c. Both filters can preserve the useful data.

The results from applying TSVMF and TVMF to stacked

data are shown in Figure 7. The difference between non-

filter, TSVMF and TVMF data are difficult to compare but

the subtracted values are easy to compare (Figure 9a and 9b).

The elliptical area in Figure 9a shows the presence of ground

roll but in Figure 9b the ground roll disappears. The other

noise, TSVMF and TVMF can reduce some refracted wave.

The random noise was decreased when data were stacked, so

it is difficult to compare with this case.

The S/N for all testing, TSVMF gives a highest S/N

value while TVMF gives a lower S/N value than TSVMF and



Figure 9 Comparison the Subtracted value between filtered data

and non-filtered data a) TSVMF data b) TVMF data.

the TVMF S/N value is close to the S/N value of non-filtered

data.

CONCLUSIONS

When considering the S/N value and subtracted value,

TSVMF is a more quality improvement for un-stacked,

deconvolved and stacked data than TVMF. The useful signal

is also preserved by both filters.

ACKNOWLEDGMENT

I am grateful to the Graduate School and the Department

of Geological Sciences, Faculty of Sciences, Chiang Mai

University for giving me the opportunity to present this work.

REFERENCES

Liu, Y., Liu, C., & Wang, D., 2009. A 1-D time-varying

median filter for seismic random, spike-like noise elimination,

Geophysics, 74(1), 17–24.

Olhovich, V. A., 1964. The causes of noise in seismic reflection and

refraction work, Geophysics, 29, 1015–1030.

Saatcilar, R. & Canitez, N., 1988. A method of ground-roll

elimination, Geophysics, 53, 894–902.

Telford, W. M., Geldart, L. P., & Sheriff, R. E., 1990. Applied

Geophysics, Cambridge University Press.Vijaykumar, V., Manikandan, S., Vanathi, P., Kanagasabapathy,

P., & Ebenezer, D., 2007. Adaptive window length recursive

weighted median filter for removing impulse noise in images

with details preservation, ECTI Transactions EEC, 6(1), 73–80.


Gas reservoir detection using three dimensional seismicattribute analysis, Gulf of Moattama, Offshore Myanmar

Soe Linn Htikea, Pisanu Wongpornchaia,∗

a Department of Geological Sciences, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, THAILAND


ABSTRACT

Economic growth in Southeast Asian countries increases in energy demand. The Gulf of Moattama, offshore Myanmar, Andaman Sea,

is one of the petroleum production areas in the Southeast Asia. This research presents the detection of gas reservoirs by integration of

seismic attributes and well-log data in the Gulf of Moattama, offshore Myanmar, Andaman Sea. Three horizons were picked with reference

to the gamma ray and resistivity logs. These horizons were defined as the top of sand layers. Well tie to seismic data was performed

and three horizons picking in the three dimensional seismic cube were carried on. Nine seismic attributes were calculated from three

dimensional seismic data and seismic attribute maps of each horizon were generated. Well-log calibrated seismic attributes were used to

generate porosity maps. Anomalies for each attributes map were located for possible potential areas. Seismic attributes maps and porosity

maps were overlaid and four possible prospect areas were delineated. Comparing between some possible prospect areas with the proved

prospected areas indicates that integration of the seismic attributes analysis and well-log data has been proved to be an effective tool in

detection of gas reservoirs.

KEYWORDS: Seismic attribute analysis, Gas reservoir detection, Gulf of Moattama, Myanmar

INTRODUCTION

Seismic attributes were introduced in the early 1970s and

became an analytical tool for qualitative prediction of geom-

etry, lithology and reservoir characteristics. By combining

seismic attributes and well-log data, the result shows the

enhanced image for interpretation or analysis. As seis-

mic attributes have a various number and variety, many

researchers have attempted to classify seismic attributes.

For example, Taner et al. (1979) classified attributes into

two categories: geometrical and physical. The geometrical

attributes (e.g., dip, azimuth, and continuity) provide the

visibility of the geometrical characteristics of seismic data.

The physical attributes (e.g., amplitude, phase, and fre-

quency) deal with the physical parameters of the subsurface

and relate to lithology. Brown (1996) classified attributes

into time, amplitude, frequency. The time-derived attributes

provide structural information, whereas amplitude-derived

and frequency-derived attributes provide stratigraphic and

reservoir information. Some attributes can be used as the

hydrocarbon indicators (Sukmono, 2007).

The objective of this study is to detect gas reservoirs

using seismic attribute analysis and geophysical well log data

in an area of Gulf of Moattama, offshore Myanmar, Andaman

Sea (Figure 1).

GEOLOGY OF THE STUDY AREA

The Andaman Sea is an extensional structure. The tectonic

evolution has taken place since Eocene (45 ma) when the

India Plate was moving eastwards towards under the Sunda

Trench (Figure 2). According to the tectonism in this

area, two major sedimentary basins have developed in the

offshore Myanmar and one of them is Moattama Basin

(Figure 2). Davies et al (2003) described the Gulf of

Moattama Basin as being bounded by north-south trending

rifted Oligocene basement and as being filled by Pliocene-

Pleistocene sediments from the Ayeyarwaddy River to the

north and highlands to the east. This late Tertiary-Quaternary

basin-fill exceeds 10 kilometers in thickness. Ayeyarwady

formation is a thick sequence of deltaic sediments deposited

by the palaeo Ayeyarwady River, comprising interbedded

claystones, siltstones and sandstones. The formation com-

prises light to medium and locally dark grey, soft to firm and

sometimes silty claystones. The claystones are interbedded

with grey-white to light grey, unconsolidated to soft and

friable sandstones. These sandstones are composed of very

fine to medium grained. Hydrocarbon potential has been

established chiefly from Ayeyarwady formation. The clastic

reservoirs are shallow marine to deltaic sandstones. The

deltaic sandstones have been deposited from the north and


Reservoir detection using 3D seismic attribute analysis

Figure 1 Study area in the Gulf of Moattama, Myanmar. (modified

from Jennings, 1997)

northeast as part of the drainage of the palaeo Ayeyarwady

delta.

METHODOLOGY AND RESULTS

The target of this study is the gas reservoirs. Three wells

have been drilled in the prospect areas. The conventional

geophysical logs were run in these wells. This study tried

to distinguish the characteristics of the attribute anomalies

of the proved prospect areas and proposed the new prospect

area which showed the similar characteristics of the attribute

anomalies as that of the proved prospect areas. The proce-

dure was described as below:

Well to well log correlation

The first step, geophysical log data were considered and

horizons in each well were selected. Well to well log

correlation was made based on gamma ray and resistivity

logs. Two horizons (H-1 and H-3) were correlated among

wells with low gamma ray and high resistivity while one

horizon (H-2) was correlated among wells with low gamma

ray and low resistivity (Figure 3). H-1 and H-3 were expected

Figure 2 Regional tectonic setting of Myanmar (modified from

Jennings, 1997)

to be the gas-bearing formation and H-2 was expected to be

the water-bearing formation.

Well to seismic correlation

A synthetic seismogram is the fundamental link between well

and seismic data. It is the main tool that allows geology to be

correlated to seismic signals. Synthetic seismograms were

generated using check-shot-corrected sonic logs, density

logs, and extracted wavelet. The wavelet was extracted

from well log data. The synthetic traces were matched with

seismic traces at the well locations and correlation between

synthetic seismogram and seismic data was calculated. An

example of matching between synthetic seismogram from

Well-1 and seismic data and correlation value were shown

in Figure 4.

Acoustic impedance - porosity cross plot

The effective porosity is the one of the main properties

indicating the potential of the reservoir. Usually, effective

porosity is measured from core sample. Since the core

sample is not always available, indirect methods to estimate


Htike and Wongpornchai

Figure 3 Well to well log correlation of three wells. Tracks 1, 3 and 5 are gamma ray logs. Tracks 2, 4 and 6 are resistivity logs.

Figure 4 Synthetic seismogram of Well-1. The blue trace is synthetic trace, the red trace is composite trace and the black trace is original

seismic trace. The correlation value is shown at the bottom of the window.

effective porosity should be performed. Acoustic impedance

is commonly used for effective porosity estimation.

To generate the effective porosity volume and map, cross

plot between acoustic impedance and effective porosity from

well log data was done and an empirical relation between

acoustic impedance and effective porosity was provided. An

example of the cross plot between acoustic impedance and

effective porosity was shown in Figure 6.

Acoustic impedance and porosity maps

Inversion is a tool to transform the seismic data into quanti-

tative property of the reservoir. Many methods of inversion

were introduced to the public. This study selected the model-

based inversion as a tool to produce acoustic impedance and

porosity volumes from 3-D seismic data. Inversion interval

was set at the time between 900 ms and 2000 ms. An example

of the acoustic impedance map for Horizon H-3 was shown

in Figure 7.



Figure 5 The seismic arbitrary line across three wells. The horizons were interpreted and shown the structural framework of the study

area.

Figure 6 Empirical relation between acoustic impedance and

effective porosity for Horizon H-3

Horizons picking on 3D seismic volume

The position of each horizon was tied from synthetic seis-

mogram to seismic data. Three horizons were picked on the

negative amplitude (trough) as the top of sand layers. These

horizons were interpreted for the entire 3D-seismic volume.

They represent the structural framework of the study area

(Figures 5). The major normal faults dip to the south and

the trend is in the east-southeast to west-northwest.

An empirical relation between acoustic impedance and

effective porosity from cross plot was used to create effective

porosity volume from seismic data. An example of effective

porosity was shown in Figure 8.

Figure 7 Acoustic impedance map

Seismic attribute maps

Seismic attribute is a quantity extracted from seismic data

that can be analyzed in order to enhance information that

might be more subtle in a traditional seismic image, leading

to a better geological or geophysical interpretation of the

data.

The two-way travel time was used as the first seismic

attribute. The map provides the possible potential areas for

future prospecting. Two-way travel time structural maps

of horizons were constructed from seismic interpretation

result. Four possible potential areas (C1, C2, C3 and C4)

were identified corresponding to high area and normal fault

closure (Figure 9). C1, C2 and C4 were located in the

position of exploration wells. C3 might be the new possible



Figure 8 Effective porosity map of Horizon H-3.

Figure 9 Two-way travel time structural map of Horizon H-3. Four

possible potential areas were indicated corresponding to the high

area and fault closure.

Figure 10 The RMS amplitude map of Horizon-H3 shows the

boundary of anomaly in pink.

Figure 11 The maximum amplitude map of Horizon-H3 shows the

boundary of anomaly in yellow.

Figure 12 The amplitude envelope map of Horizon-H3 shows the

boundary of anomaly in yellow.

potential area.

To increase the level of confidence, nine attribute maps

(RMS amplitude, maximum amplitude, amplitude envelope,

apparent polarity, instantaneous frequency, quadrature trace,

instantaneous phase, cosine of instantaneous phase, and

effective porosity) for three horizons were calculated with the

window length of 20 ms. The attribute maps of the Horizon

H-3 were used as the example.

The RMS amplitude map with its anomalies and bound-

aries of possible potential areas was shown in Figure 10.

The maximum amplitude map with its anomalies and

boundaries of possible potential areas was shown in Figure

11.

The amplitude envelope map with its anomalies and




Figure 13 The apparent polarity map of Horizon-H3 shows the

boundary of anomaly in green.

Figure 14 The instantaneous frequency map of Horizon-H3 shows

the boundary of anomaly in yellow.

12.

The apparent polarity map with its anomalies and bound-


The instantaneous frequency map with its anomalies and


14.

The quadrature trace map with its anomalies and bound-


The instantaneous phase map with its anomalies and


16.

The cosine of instantaneous phase map with its anoma-

lies and boundaries of possible potential areas was shown in

Figure 17.

The effective porosity map with its anomalies and


Figure 15 The quadrature trace map of Horizon-H3 shows the

boundary of anomaly in blue.

Figure 16 The instantaneous phase map of Horizon-H3 shows the

boundary of anomaly in blue.

18.

Combination of these anomalies with boundaries of

possible potential areas in the two-way travel time map was

shown in Figures 19 and 20.


Discussion

Seismic attribute analysis was done in the study area of

offshore Myanmar. Three horizons (H-1, H2, and H-3) were

defined and 3-D seismic data were interpreted. The Horizon

H-3 was used as an example for this study. Four possible

potential areas were identified from two-way travel time

map. Three possible potential areas (C1, C2 and C4) were

located in the exploration well locations and another possible

potential area (C3) has not been drilled. The combination

of anomalies of seismic attributes with two-way travel time



Figure 17 The cosine of phase map of Horizon-H3 shows the

boundary of anomaly in green.

Figure 18 The effective porosity map of Horizon-H3 shows the

boundary of anomaly in red.

map shows that most of anomalies of seismic attributes were

located in the possible potential areas. Three wells were

drilled in three possible potential areas with the satisfied

result. This study indicates that another possible potential

area might be the next target of exploration.

Conclusion

Integration of well log data and seismic attribute analysis

is one of the powerful tools for indicating the gas-bearing

reservoir in seismic exploration. It can scope and show the

possible potential area for the future exploration. It can help

the reduction of the exploration cost and increasing the level

of confidence in seismic exploration.

ACKNOWLEDGMENT

Authors are grateful to the Graduate School and the Depart-

ment of Geological Sciences, Faculty of Sciences, ChiangMai University for giving us the opportunity to present this

work.

REFERENCES

Brown, A. R., 1996. Interpretation of three-dimensional seismic

data (Forth Edition), American Association of Petroleum Geolo-

gists, 42, 223–284.

Davies, R., Medwedeff, D., & Yarwood, D., 2003. Structural

trap and fault-seal analysis, offshore Myanmar: A case study,

AAPG/Datapages Discovery series, 7, 157–178.

Jennings, B., 1997. Final report Blocks M9 and M7 Gulf of

Martaban Myanmar.

Sukmono, S., 2007. Complex attributes for DHI & reservoir

analysis, Seismic Courses, pp. 1–131.

Taner, M., Koehler, F., & Sheriff, R., 1979. Complex seismic trace

analysis, Geophysics, 44, 1041–1063.



Figure 19 Combination of attribute anomalies of RMS amplitude, maximum amplitude, amplitude envelope, apparent polarity, and two-

way travel time map of Horizon H-3.

Figure 20 Combination of attribute anomalies of instantaneous frequency, instantaneous phase, cosine of instantaneous phase, quadrature

trace, and two-way travel time map of Horizon H-3.


Porosity and Permeability Estimation from SeismicAttributes by Multi-layer Feedforward Neural NetworkTechnique in an Area of Gulf of Thailand

Theerachai Norkhamboota,∗, Pisanu Wongpornchaia

a Department of Geological Sciences, Faculty of Science, Chiang Mai University, Chiang Mai, THAILAND


ABSTRACT

In this study, seismic attribute analysis was used to estimate porosity and permeability of reservoir in an area of Gulf of Thailand. An

interesting sandstone layer was identified with the aid of well log data. Acoustic impedance volume was created as an external attribute

for seismic attribute analysis. To improve the ability of porosity and permeability estimation, the best attributes of multi-attribute analysis

results were computed using multi-layer feedforward neural network technique. To verify the multi-layer feedforward neural network

technique, the cross-plot analysis of multi-layer feedforward neural network results were performed and found that the correlation between

the predicted porosity and actual porosity gave a correlation value of about 0.95 with an average error value of 0.016. The multi-layer

feedforward neural network result of the correlation between the predicted permeability and actual permeability result gave correlation

value of about 0.99 with an average error value of 318.44 The analysis of results from multi-layer feedforward neural network technique

were shown that they are an effective technique to estimate porosity and permeability in a reservoir.

KEYWORDS: Seismic attribute analysis, Well log data, Multi-layer feedforward neural network, Gulf of Thailand

INTRODUCTION

Porosity is an important property of a reservoir because

hydrocarbon (oil and gas) can fill in voids of porous rocks.

Permeability is essential requirement information for reser-

voir evaluation because it is the ability of fluids to pass

through the pores in a material. The estimation of reser-

voir properties has been continuously developed for many

years. The prediction of physical properties such as porosity

from empirical correlations of multivariate linear regression

between seismic attributes and well log data was introduced

by numerous authors (Schultz et al., 1994; Schultz et al.,

1994a; Russell et al., 1997; Hampson et al., 2001). A seismic

attribute analysis to estimate physical properties such as

porosity, permeability and others in a reservoir were studied

(Brown, 1996; Leiphart and Hart, 2001; Tebo and Hart, 2003;

Calderon and Castagna, 2007). In this study, seismic attribute

analysis was applied to estimate porosity and permeability

in a gas sandstone layer. Acoustic impedance volume as

an external attribute was created and the internal attributes

were computed from seismic data. Step wise regression

method was used to find the best internal attributes. These

attributes were applied in multi-layer feedforward neural

network to estimate porosity and permeability. The attribute

map from multi-layer feedforward neural network result can

be used to interpret porosity and permeability related to

spatial distribution of a gas sandstone layer.

STUDY AREA

Cenozoic basins in the Gulf of Thailand consist of non-

marine to marginal marine Tertiary strata and the non-marine

sandstone reservoirs deposited in river system of fluvial and

lacustrine deltaic environments (Pradidtan and Dook, 1992).

The study area is one of the important petroleum production

fields in the Gulf of Thailand.

METHODOLOGY

A gas sandstone layer was selected from 2 well log data.

It was identified by low gamma ray (green curve), high

resistivity (red curve) in the first track, and crossover between

neutron porosity (blue curve) and density porosity (red curve)

in the second track (Figures 1 and 2). The interesting sand-

stone layer at well ZA was selected at the measured depth

of 1726.1 to 1741.64 m (black straight line) (Figure 1) and

at well ZB was selected at the measured depth of 1536.08 to

1545.29 m (black straight line) (Figure 2). The total porosity

was computed from neutron and density porosity logs. Since

clay minerals in sandstone closed the connection of pores in


Porosity and permeability multi-layer feedforward neural network

Figure 1: The interesting sandstone layer was picked at well

ZA. The blue seismic trace is the synthetic seismic trace. The

red seismic trace is the composite seismic trace. Two yellow

lines are a window of analysis.

Figure 2: The interesting sandstone layer was picked at well

ZB. The blue seismic trace is the synthetic seismic trace. The

red seismic trace is the composite seismic trace. Two yellow

lines are a window of analysis.

sandstone, effective porosity was needed to calculate from

total porosity which shown in the third track (red curve)

(Figures 1 and 2). The irreducible water saturation defines

the maximum water saturation that a formation can retain

without producing water. The permeability was computed

from effective porosity and irreducible water saturation that

shown in the fourth track (magenta curve) (Figures 1 and 2).

Well to seismic correlation was done by synthetic seismo-

grams (Figures 1 and 2). Synthetic seismogram (blue line)

was generated by convolution between acoustic impedance

(product of check shot corrected sonic and density logs) and

extracted wavelet from seismic data. The synthetic seismic

traces were correlated with composite seismic traces (red

line) at the wells ZA and ZB. Acoustic impedance volume

was created by inversion between acoustic impedance log

Figure 3: The acoustic impedance inversion result of well

ZA and well ZB. The blue curve is original log (acoustic

impedance log). The red curve is inverted result. The

brown curve is initial model. The green curve is constraints.

The black and gray lines are top and base of horizons,

respectively.

(a) The attribute list for porosity prediction of the multi-

attributes analysis result

(b) The multi-attribute attribute analysis result shows the

average RMS error and validation error.

Figure 4

and seismic volume. The seismic attributes were computed

from seismic volume and compared with actual effective

porosity log and actual permeability log for the maximum


Norkhamboot and Wongpornchai

(a) The attribute list for permeability prediction of the multi-

attributes analysis result.

(b) The multi-attribute attribute analysis result shows the

average RMS error and validation error. The optimum

number of attribute is equal to 4.

Figure 5

Figure 6: The cross plot between the predicted porosity and

actual porosity from multi-layer feedforward neural network.

correlation value of the best single attribute. The best

single attribute and other attributes were inputted into step

wise regression method for maximum correlation value and

minimum validation error value of the best multi-attributes

(Hampson et al., 2001). The best multi-attributes were ap-

plied in multi-layer feedforward neural network to generate

effective porosity and permeability volumes and maps of a

sandstone layer.

Figure 7: The cross plot between the predicted permeability

and actual permeability from multi-layer feedforward neural

network.

Figure 8: The average porosity map of the interesting

sandstone layer.

RESULTS

A sandstone layer was selected for porosity and permeability

estimation. Well to seismic correlations were displayed a

correlation value of about 0.932 in well ZA and 0.936 in

well ZB. Figure 3 displayed the correlation result between

acoustic impedance log and inversion result (well ZA and

well ZB). It showed the correlation value of about 0.99. For

porosity, the best multi-attributes from step wise regression

method were filter 5/10-15/20, filter 45/50-55/60, derivative

instantaneous amplitude, instantaneous phase and second

derivative instantaneous amplitude (Figure 4). For perme-

ability, the best multi-attributes from step wise regression

method were filter 5/10-15/20, 45/50-55/60 and amplitude

weighted frequency (Figure 5). The best multi-attributes

were applied in multi-layer feedforward neural network for


Porosity and permeability multi-layer feedforward neural network

Figure 9: The average permeability map of the interesting

sandstone layer.

porosity and permeability estimation. The cross-plot of

multi-layer feedforward neural network result between the

predicted porosity and the actual porosity is shown in Figure

6. The cross correlation value is about 0.95 with an average

error value of about 0.016. The cross-plot of multi-layer

feedforward neural network result between the predicted

permeability and the actual permeability is shown in Figure

7. The cross correlation value is about 0.99 with an average

error value of about 318.44. The multi-layer feedforward

neural network results were applied to generate porosity and

permeability maps of a sandstone layer (Figures 8 and 9)

CONCLUSION AND DISCUSSION

Porosity and permeability estimation using seismic attribute

analysis in a sandstone layer using multi-layer feedforward

neural network technique was successful. The porosity and

permeability maps generated from the analysis result of

multi-layer feedforward neural network presented the best

correlation with the value of 0.95 and 0.99, respectively. The

high porosity and permeability areas were confirmed by the

position of the well ZA and ZB. The new development areas

can be considered using the combination of the porosity and

permeability maps.

ACKNOWLEDGEMENT

The authors gratefully acknowledge the Graduate School,

Chiang Mai University for giving the scholarship to support

this research work. We would like to thank Department

of Geological Sciences, Faculty of Sciences, Chiang Mai

University for equipment support.

REFERENCES

Calderon, J. & Castagna, J., 2007. Porosity and lithologic

estimation using rock physics and multi-attribute transforms in

Balcon field, Colombia, The Leading Edge, 26, 142–150.Hampson, D., Schuelke, J., & Quirein, J., 2001. Use of multi-

attribute transforms to predict log properties from seismic data,


Leiphart, D. & Hart, B., 2001. Comparison of linear regression

and a probabilistic neural network to predict porosity from 3-D

seismic attributes in lower brushy canyon channeled sandstones,

southeast New Mexico, Geophysics, 66, 1349–1358.

Pradidtan, S. & Dook, R., 1992. Petroleum geology of the northern

part of the Gulf of Thailand, in: Piancharoen, c. (ed.), National

Conference on Geologic Resources of Thailand: Potential for

Future Development, Department of Mineral Resources, 17-24

November, Bangkok, Thailand, pp. 235–245.

Russell, B., Hampson, D., Schuelke, J., & Quirein, J., 1997. Multi-

attribute seismic analysis, The Leading Edge, 16, 1439–1443.

Schultz, P., Ronen, S., Hattori, M., & Corbett, C., 1994. Seismic-

guided estimation of log properties: Part 1: A data-driven

interpretation methodology, The Leading Edge, 13, 305–315.

Tebo, J. & Hart, B., 2003. 3-D seismic attribute study for reservoir

characterization of carbonate buildups using a volume-based

method, CSPG/CSEG Joint convention, June 2-6..


Model-based Inversion of Magnetotelluric (MT) Data in theFang Basin

Khin Moh Moh Latta,, Pham Huy Giaoa,∗

a Geoexploration & Petroleum Geoengineering (GEPG) Program, Asian Institute of Technology


ABSTRACT

With the increasing onland petroleum exploration activities in Thailand, the question whether the electromagnetic (EM) methods (in this

case the magneto-telluric method, MT) can be a useful tool for deeper hydrocarbon exploration at a small mountainous basin like the Fang

basin is worth studying. As no EM field survey has ever been conducted at this site and no real field data set are available. This research

is focused only on approach of model-based inversion of the synthetic MT data for an earth model. One-dimensional inversion using

smoothness constraint was conducted. The model-based inversion results look quite close to the synthetic model, suggesting a possible

application of this method for the Fang basin in future.

KEYWORDS: Fang basin, onland exploration, Magnetotellurics (MT), model-based inversion, synthetic model

INTRODUCTION

This paper introduced the application of an inversion algo-

rithm with smoothness constraint based on a code developed

by Sasaki (2009), which can generate one-dimensional re-

sistivity structure of a prior earth model. The case study

is on the Fang Basin, Northern Thailand. First, the earth

model of Fang basin was built up based on the interpreted

seismic data and stratigraphy. Then, the theoretical MT

response was calculated by Maxwell’s equation in a forward

modeling, and it was further inverted by one-dimensional

inversion algorithm, in which layer resistivities could vary

but thickness values were fixed. The calculation begins

with the forward computing of responses along with the data

misfit. After that, the Jacobian matrix is computed using a

chain rule. Next, the regularization (tradeoff) parameter β is

selected by the user. In this case, several trials were made to

find out a suitable value of β, for which the value of 0.01 was

chosen throughout this study. Finally, the model parameters,

i.e., resistivities, were calculated by using the modified Gram

Schmidt method. The algorithm continues to iterate until the

calculated response matches the synthetic response.

FORWARD MODELING

The purpose of forward modeling is to calculate the theoreti-

cal earth responses, i.e. the apparent resistivity and phase, for

different frequencies. The input parameters include number

of layers, and corresponding resistivity, depth and frequency

values. Scientists in the 1950s realized that measuring the

time-varying electric and magnetic fields at a given location

could result in repeatable calculations of the Earth’s geoelec-

tric properties at that location (Tikhonov, 1950; Cagniard,

1953). The primary magnetic fields generate secondary

electric and magnetic fields within electrically conductive

material in the Earth, and the depth at which currents are

induced is dependent on the frequency of the field. Thus, by

measuring a broad spectrum of electric and magnetic fields

it is possible to infer Earth’s conductivity as a function of

depth.

The governing equations of the MT method can be

derived by following Maxwell’s equations:

∇× E = −∂B

∂t(1a)

∇×H = J +∂D

∂t(1b)

Where:

E = the electric field intensity (V/m),

H = the magnetic field intensity (A/m),

B = µH = the magnetic induction, or flux density (Wb/m2

or tesla),

µ = the magnetic permeability (H/m),

J = σE = electric current density (A/m2),

σ = the electrical conductivity (mho/m),

ǫ = the dielectric permittivity (F/m).


Model-based inversion of magnetotelluric data

The MT signal originates at a substantial distance in the

ionosphere and magnetosphere, the source field is assumed

to be a plane wave. Similarly, the large contrast in resistivity

between the earth’s atmosphere, which is very resistive, and

the earth’s surface, which is very conductive, requires that the

electromagnetic waves propagate vertically below the Earth’s

surface independent of their origin in the ionosphere. For

homogeneous layer, the impedance (Z) can be determined by

measuring the horizontal electric field (E) and the magnetic

field (H) at the surface or on the seabed (Brady et al., 2009).

The wave impedance of an electromagnetic wave is

the ratio of the transverse components of the electric and

magnetic fields. Maxwell’s equation in frequency domain

can be shown as below:

∇× E = −iωµH

∇×H = (σ + iǫω)E + J(2)

The displacement currents (iǫµ) can be neglected in the

quasi- static approximation. Therefore, the ratio of the

electric field (Ex) to the magnetic field (Hy) determined the

surface impedance, Z as shown below:

Zi =Ex

Hy

=√

iωµρi (3)

The complex impedance (Zi) can be calculated to obtain the

apparent resistivity (ρa) and the phase angle (Phi), between

the E and H fields (Grandis et al., 2004).

ρa =1

µω|Zi|2 (4)

Here, the magnetic permeability of free space (µ = µ0) is

assumed for all Earth materials. The complex impedance

(Zi) can be written as: (Sharma, 1986)

Zi = RS + iωLS (5)

phase angle (Φ) can be determined as:

Φ = tan−1

(

ωLS

RS

)

Φ = tan−1

(

ImZ

ReZ

) (6)

MODEL-BASED INVERSION METHOD

Generation of synthetic data and objective function

First, the forward responses (i.e., apparent resistivity and

phase) from the geomodel will be calculated. Second, by

giving the initial inversion model as an input, the forward

responses will be generated from the forward subroutine.

From the synthetic data and calculated data, the objective

function can be identified as follows:

φd(m) =

N∑

j=1

[dj − Fj(m)]2

(7a)

Where,

φd(m) = objective function or data misfit ,

dj= synthetic data (in-phase and quadrature),

Fj(m) = model response.

When the first-order Taylor expansion is applied, the objec-

tive function becomes:

φd(m) =

N∑

j=1

[

∆dj −M∑

i=1

∂Fj

∂mi

∆mi

]2

(7b)

In matrix form of equation 7b,

φd = ‖A∆m−∆d‖2 (8)

Where,

A = N ×M Jacobian matrix,

∆mi = model parameter change, which is unknown,

∆dj = dj − Fj(m(0)) = different between observed data

and model response.

Calculation of the data misfit, (RMS)

In defining the data misfit, the model responses have to

be weighted. To avoid the weighting, ρa cosφ/ρobsa and

ρa sinφ/ρobsa are used. In these expressions, the normaliza-

tion by the observed apparent resistivity means that the data

misfit is defined in terms of the relative difference, but not

the absolute difference.

RMS =

√

X2

2(9a)

Where, x2 =M∑

j=1

(dj − Fj(m))2

RMS =

√

√

√

√

√

M∑

j=1

(dj − Fj(m))2

2(9b)

Calculation of the smoothness constraint

To find a model that fit the data as well as incorporates

additional information, design the total objective function

that includes a model objective function and the data misfit.

Then the total objective function becomes as follows:

φ = φd + βφm (10)

Where,


Latt and Giao

φd = the objective function or data misfit between observed

data and model response,

φm = model objective function or constraints,

β = tradeoff (regularization) parameter

In this study, first derivative and second derivative constraints

are selected for model objective functions (see Table 1).

Models smoothed with a first derivative operator tend to

be flat as possible, and models smoothed using a second

derivative operator prefer structure with a constant slope. The

main difference between the inversion algorithm described

in this research and the one outline here is definition of the

model roughness or smoothness.

Finally the total objective function becomes:

φ = ‖A∆m−∆d‖2 + β‖Cm‖2, (11)

where m = m(0) +∆m.

When the total objective function is minimized:

‖A∆m−∆d‖2 = 0

A∆m = ∆d

and

β‖Cm‖2 = 0√

βC(m(0) +∆m) = 0√

βC∆m = −√

βCm(0).

In matrix form,

[

A√βC

]

∆M =

[

∆d

−√βCm(0)

]

(12)

In this case, the model parameters, ∆m, are the logarithm

of the resistivity in order to impose the positivity and these

can be obtained by using the modified Gram-Schmidt (least

square) method.

EFFECT OF THE REGULARIZATION PARAMETER

(β)

Khin et al., (2011) tested the effect of regularization param-

eter (β) on two models: model A for a conductive wedge

and model B for a resistive wedge. According to Khin et al.,

(2011) research, the β value is selected 0.01 which gave the

smallest data misfit among trial values.

APPLICATION IN THE FANG BASIN, NORTHERN

THAILAND

Geology of the Fang Basin

The Fang basin is located near the Myanmar border, and it

is around 18km wide and 40km long. Based on gravity and

seismic surveys interpretation, the Fang basin can be divided

into 3 extensional sub-basins. Since 1956, the Defense

Figure 1 Location of Fang Oil Field (Modified from Morley, et al.,

2000).

Figure 2 Lithostratigraphic Succession of the Fang basin.


Model-based inversion of magnetotelluric data

Figure 3 The earth model of Fang basin used in forward modeling.

Figure 4 Convergence characteristic of the inversion at β = 0.01.

Table 1 Model objective functions

First derivative Second derivative

φm = ‖ ∂m∂z

‖2 = ‖Cm‖2 φm = ‖ ∂2m

∂z2‖2 = ‖Cm‖2

C =

1 −1 0 0

0 0 0 0

0 0 1 −1

C =

1 −1 0 0 0

−1 2 −1 0 0

0 −1 2 −1 0

0 0 −1 2 −1

0 0 0 −1 1

Energy Department (DED) of Thailand has developed this

field. A new age of high technologies of geological survey,

3-D seismic survey, and directional drilling wells have been

applied in this field (Settakul, 2009). From the geological

data, the formation comprises upper zone of Maefang and

lower zones of Maesod formations.

Maefang formation

The Maefang formation overlies the Maesod formation and

comprises 300-500 m thick of coarse arkosic sandstones with

minor interbedded shales. Sizes of sands vary from coarse to

very coarse grains (Settakul, 2009).

Maesod formation

Maesod formation can be subdivided into lower Maesod

and upper Maesod formations. Lower Maesod formation

(early Miocene) is composed of brown to gray shale, coal

and sandstone. Organic shale in the central part of the

basins is a potential source rocks. Upper Maesod formation

(middle- late Miocene) consist of four packages of sand,

however only two packages of sands have been proven to be

production sands, which thicknesses varies from 1-10 m. All

oil productions come from the Maesod formation. Moreover,

the interbedded sand and shale of the Maesod formation

make the seal nature. The thick shale effectively seals the

sands from each other. Therefore, Maesod formation is

composed of source rock, reservoir rock and seal (Settakul,

2009).

The EM geomodel for the Fang Basin

Based on stratigraphy and seismic data, the number of layers,

thickness and types of rocks can be identified. In this case,

the resistivity value for each layer is estimated from the well

logging data with reference to Parkpum (2010).

Figure 5 The MT responses calculated for the Fang basin.


Latt and Giao

Figure 6 Comparison of the true and inversion models for the Fang

basin.

The initial inversion model has 75 layers, overlying a

homogeneous half-space of 10 Ωm. The inversion model

is converged at fourth iteration with RMS misfit of zero, as

shown in Figure 4.

The MT synthetic responses of Fang basin are shown in

Figure 5. Comparison of the true and inversion models with

a maximum smoothness in a first derivative (solid line) anda second derivative (dash line) is shown in Figure 6. Both

first and second derivative constraints could create the best-

fit inversion model with the true model.

CONCLUSIONS

The inversion algorithm was tested on earth model and it

is very stable and typically converges within four or five

iterations. The results of model-based inversion from first

derivative and second derivative methods are very close

to the constructed Fang geomoel. It is expected that the

model-based inversion adopted in this study could be used

to investigate the MT responses and the resistivity model

structure of Fang basin.

REFERENCES

Brady, J., Campbell, T., Fenwick, A., Campbell, C., Ferster, A., &

Labruzzo, T., 2009. Electromagnetic sounding, Oilfield Review,

21, 4–19.

Cagniard, L., 1953. Basic theory of the magnetotelluric method of

geophysical prospecting, Geophysics, 18, 605–635.

Grandis, H., Widarto, D. S., & Hendro, A., 2004. Magnetotelluric

(MT) method in hydrocarbon exploration: A new perspective,

Journal Geogisika, 2, 14–19.

Khin, M., Giao, P. H., & Sasaki, Y., 2011. Model-based inversion

of MT responses for a deep fractured granite reservoir in theCuu Long basin, in Proceedings of the 10th SEGJ International

Symposium.

Parkpum, A., 2010. Integrated interpretation of Well Logging Data

with Reference to Reservoir Characterization of the Fang Oil

Field, Master’s thesis, Asian Institute of Technology, Bangkok,

Thailand.

Settakul, N., 2009. Fang oilfield development, The Journal of the

Walailak Science and Technology, 6, 1–15.

Sharma, P., 1986. Application of geophysical methods to engineer-

ing and environmental problems, Geophysics, pp. 301–308.

Tikhonov, A. N., 1950. Determination of the electrical character-

istics of the deep strata of the earth’s crust, Dok. Akad. Nauk.

SSSR, 73, 295–297.


Geological Structures related to Hot Springs in Krabi,Southern Thailand

Usa Nilsuwana,b,∗, Helmut Durrasta,b

a Geophysics Research Center, Prince of Songkla University, HatYai, Songkhla, 90112, THAILANDb Department of Physics, Faculty of Science, Prince of Songkla University, HatYai, Songkhla, 90112, THAILAND


ABSTRACT

For Krabi Province aeromagnetic data were analyzed and gravity surveys were carried out there in order to identify shallow and deeper

structures, mainly the heat source and possible faults that allow the ascent of hotter fluids towards the surface, and by this creating several

hot springs. The aeromagnetic interpretation that covered the whole Krabi Province showed anomaly values ranging from -283 to 159 nT,

which can be correlated to igneous rocks from north to south at the near surface according to the geological map from the Department of

Mineral Resources (2007), namely granitic rock with a depth of 2 km, and a syenite rock with a depth of 1 km in the model. Due to their

relatively small volume they are not considered as the main heat source. The gravity survey with 101 stations presents the final Bouguer

values ranging from -82 to 133 g.u. The Bouguer anomaly map shows very low values in the central part of the Krabi Basin and a trend

with lower values to the Andaman Sea. Horst and graben structures correlate to the hot spring manifestation with a maximum depth of the

Tertiary basin of about 700 m. Higher Bouguer anomaly values at the boundaries of the Krabi Basin in the N and ESE where related to

the Triassic-Cretaceous rock, Permian limestone, dolomite and some areas in the eastern part correlate to the syenite rock. The shallower

geological structures of the geothermal area in Krabi Province are compared with seismic and borehole data from lignite exploration in

1982 by the Electricity Generating Authority of Thailand. These are considered as quite complex due to several faults as well as major

horst and graben structures. Results from both studies are in remarkable agreement.

KEYWORDS: Hot springs, Krabi, Aeromagnetic, Gravity, Tertiary Basin

INTRODUCTION

Krabi Province is located in the southern part of Thai-

land about 990 kilometers south of Bangkok along the

Phetkasaem Highway (Markirt et. al., 1984). The study

area is located between 859000 to 915000 N and 482000

to 527000 E of Zone 47, UTM coordinate system based

on WGS-84, and by this, it is covering an area of around

2,520 square kilometers. There are five hot springs in

Krabi Province, Ban Hoi Yung Tok (KB1), Khlong Boe

Nam Ron (KB2), Bang Pueng (KB3), Ban Nam Ron (KB4),

Saphan Yung hot waterfall (KB5) that are located in Nua

Khlong and Khlong Thom District (Figure 1). Near surface

groundwater temperatures of 40-51 °C have been measured

at several discharge sites but the temperature is not enough

to compensate traditional energy resources, therefore the

current use is focusing on recreational, healing, and tourism

purposes (Figure 2). Currently, the hot spring areas in

Krabi Province are being widely used for thermal healing

and tourism purposes but they are not developed seriously.

Therefore, an understanding of the geological structures of

the hot spring areas in Krabi Province would be useful for

any further development. This will provide fundamental

and necessary information for any direction for the use of

geothermal energy in the future.

The main objective of this study is to understand the

shallow geological structures of the geothermal area in Krabi

Province comprising Krabi Basin and adjacent areas. This

includes the understanding of the pathways of the hot spring

waters probably related to several minor faults, and horst and

graben structures, and the delineation of the geometry of the

(shallow) geothermal reservoirs, as well as any possible heat

source of the hot spring at depth.

Active geothermal systems are characterized by high

subsurface temperatures which are the signs of heat/fluid

up flow. Since geothermal systems are complex from the

geological and hydrological point of view and identification

of permeable and impermeable horizons and of geologic

structures that control fluid circulation would facilitate tar-

geting wells at economic depths. A single prospecting

method cannot characterize them accurately; a combination


Geological structures related to hot springs

Figure 1 Five hot springs site in Krabi Province (stars in red).

Topographic map as base.

Figure 2 Five hot springs in Krabi Province: KB1-Ban Hoi Yung

Tok, KB2-Khlong Boe Nam Ron, KB3-Bang Pueng, KB4-Ban Nam

Ron, KB5-Saphan Yung hot waterfall.

of techniques should therefore be employed and a variety of

heat sources for many hot springs in Thailand are discussed

by Raksaskulwong and Thienprasert (1995).

This work utilizes mainly surface gravity and aeromag-

netic methods in order to provide the required geological

information at depth. Results might be used for any devel-

opment scheme of the hot spring areas in Krabi Province

in the future. Aeromagnetic geophysical measurements are

commonly applied for geological mapping of an interested

area in order to determine surface and subsurface geological

structures. This method can be used for a rapid coverage of

large areas, interesting areas pinpointed, regional anomaly

pictures and geological mapping of unknown areas can

be directed. A prior work based on aeromagnetic data

in Sankamphaeng was associated with active faults in the

vicinity of the Sankamphaeng hot spring and also in Surat

Thani the magnetic anomaly might be related to a heat source

of the hot springs there (Wisedsind, 1997). Aeromagnetic

data interpretation from Ranong Province showed a negative

magnetic anomaly near the RN6 hot spring with a surface

temperature of 80 degreeC and this may be related to the

heat source (Khoonphunnarai, 2008). The gravity method

is a geophysical technique that measures differences in the

earth’s gravitational field at different locations due to differ-

ent earth materials have different densities (mass). The hot

springs distributed in northern Thailand are believed to be

associated with a granitic intrusion of possibly Cretaceous-

Tertiary age or rejuvenated young plutonic or late basaltic

eruption (Chuaviroj, 1988) and the interpretation of gravity

data from the Chiang Mai Basin area indicated that this

Tertiary basin can be divided into five sub-basins where 2.5

D modeling suggested that the depth to the Cretaceous base-

ment varies from 1.3 km at the northern end to 2.3 km at the

southern end (Wattananikorn et al., 1995). On the other hand,

the hot spring at Chantaburi Province in eastern Thailand

are likely related to and controlled by faulting where the

basalts are located directly at the fault (Charusiri, 2003).

For the hot springs in Surat Thani Province in the southern

part of Thailand it is suggested that are correspond with the

intersection of a NW-SE and a NE-SW fault system and

are related to horst and graben structures (Khawdee, 2008;

Khawtawan, 2008), the hot springs in Ranong Province were

probably caused by cold meteoric waters circulating in the

subsurface that was heated up by an igneous body; and

then the hot water is transported along fault zones to the

surface, appearing as hot springs (Sanmuang et al., 2007)

and the hot spring areas at Khao Chaison District, Phattalung

Province suggested that the hot springs were associated with

shallow Permian limestone, likely a part of horst and graben

structures, and that the faults are the pathway for the hot

water from a deeper heat source (Jonjana, 2009).

GEOLOGICAL AND TECTONIC SETTING

The Krabi area is near the region of a major fault zone,

the Khlong Marui Fault and Ranong Fault Zone, which

were formed in a north-to-northeast-trending transpressional

tectonic setting and cut across the Thai Peninsula south with

NW-trending faults (Leow, 1985; Watkinson et al, 2008).

The geology of Krabi Province can be classified in fol-

lowing stratigraphical sequences (Figure 3): Carboniferous-

Permian or Permo-Carboniferous (CPk) is the oldest group

in this area, Permian (Pr) is also know as Ratburi Group

with the general character of this limestone is massive beds

with nearly horizontal inclination, Triassic (Trl) or Lampang

Group and it is called ‘Sai Bon Formation’ and the rock

matrices where close to hot spring sites are more solidified

due to the cementation of silica/carbonates in the water from

hot springs, Jurassic (Jk) is called ‘Khlong Min Formation’,

Jurassic-Cretaceous (JKl) is separated into two formations

namely, the Lam Thap Formation that overlies the Sai Bon


Nilsuwan and Durrast

Formation and the Khlong Min Formation and the Sam

Chom Formation overlies the Lam Thap Formation. This

formation is located at steep cliffs of the high mountains

in Kao Krop Kra Ta and Kao Luk Kai. Sedimentological

structures can be found, like graded bedding and cross

bedding. The pre-Tertiary basement rocks are believed

clastic sediments of Carboniferous and Permian. Subsurface

geological evidence indicates that the basement rocks are

Permian limestone of Ratburi Group and Mesozoic clastic

rocks of Khorat Group. For the Tertiary (Tkb) is located and

especially in the Krabi basin and the formation filling in the

Krabi basin is called ‘Krabi Group’ and can be separated in

five formations, A to E (Figure 4a), and Quaternary (Q) is

the youngest group in the area located at topmost and the

youngest sediment deposits compose of gravel, sand, silt and

clay, and unconsolidated sediments that occurred from the

erosion of underlying rock units and have been transported

by rivers and streams. For the Krabi basin, this is called

‘Q group’ that is overlying the Krabi group. Igneous rocks

appear in three areas a part of Khao Phanom Benja is a

batholith granite intrusion identified as a porphyry granite

or biotite-hornblende granite, Khao Lak Kai granite can be

found as small stock granites identified as a granodiorite

with main accessory minerals being quartz, feldspar, biotite

and hornblende, and Kuan Nok Wa rhyolite/syenite is an

intrusion located at the Krabi basin boundary in the north

eastern part and it is identified as a porphyry rhyolite/syenit

with the main porphyry mineral being feldspar (Leow, 1985;

Sripongpan et. al., 1990; Chaimanee and Tanpisit, 1991;

Department of Groundwater Resources, 2006; Department

of Mineral Resources, 2007).

According to the work done during the exploration for

lignite in the Krabi basin by Longworth-CMP Engineering

for EGAT in the 1980s, the structure within the basin is

considered as the most complex of the region and largely

unknown due to lack of outcrop or other information. The

graben structures are present throughout these fault zones

with a maximum depth from surface to present pre-Tertiary

basement of over 600 m. The pre-Tertiary basin basement

rocks are believed to be the clastic sediments of Carbonif-

erous and Permian. The occurrence of Krabi basin can be

separated into sixth stages (Figure 4b);

(i) erosion phase of pre-Tertiary rocks and basinal fault (Fm)

development in NE-SW direction with horse and graben

structures,

(ii) gentle subsidence and silting up in some parts and F1

fault development in NW-SE direction with the deposi-

tion of A formation by fluvio-lacustrine sediments,

(iii) gentle subsidence and F2 fault development in NE-

SW direction with lacustrine-coal swamp environment

leading to B formation,

(iv) gentle subsidence in Miocene Era with deposition of

lacustrine and fluvio-lacustrine sediments, C formation,

Figure 3 Geology of Krabi Province (Department of Mineral

Resources, 2007); dashed black lines - aeromagnetic cross sections,

solid black lines - gravity cross sections.

(v) most active F2 faulting in NE-SW direction and F3

fault development in N-S direction; differences in the

development and sediment deposition in Krabi basin; in

the northern part local uplift occurred with the develop-

ment of an unconformity and the deposition of fluvio-

lacustrine sediments, D formation; in the southern part

still gentle subsidence and marine transgression occurred

with the deposition of deltaic sediments, E formation,

and

(vi) regional uplift and the basin was covered by unconsol-

idated sediments (Longworth-CMP Engineering, 1982;

Leow, 1985).

RESEARCH METHODOLOGY

The aero survey took place during the period from February

1985 to March 1987 by Kenting Earth Sciences International

Ltd. under the Mineral Resources Development Project of

the Department Mineral Resources, Thailand. The data are

the followings; a part of the C1 area of regional airborne

magnetic surveys. They are in nine map sheets of 1:50,000

scales, namely; 4724-I, 4725-I and -II, 4824 -I and -IV, 4825-

I to IV. The average traverse line spacing was 1 km in east-

west and the spacing of control lines was approximately 14

km and the flight altitude was 400 feet mean terrain clear-



Figure 4 General characteristics of the Krabi basin; (a) Different

formations in the Krabi basin at depth; A - reddish-brown and

grey claystone, siltstone and white to grey sandstone interbedded

with some limestone and carbonaceous claystone; B - grey to

greenish-grey to grey claystone, sandstone, limestone carbonaceous

claystone and coal; C - grey to greenish-grey claystone, white to

grey sandstone, siltstone, grey limestone, and it contains abundant

fresh water fossils, like gastropods; D - greenish-grey to grey to

reddish brown claystone, grey to white sandstone and grey siltstone;

E - grey to reddish-brown claystone, grey to white and fine to coarse

grain sandstone and a few carbonaceous claystone in decreasing

order of abundance; GR - Group, MBR - Member; (b) Steps of

Krabi basin development (Leow, 1985).

ance. For magnetic data, 1980 IGRF field was calculated at

each data pointed and subtracted from the original magnetic

data at each point. The magnetic anomaly of the study area

was plotted as magnetic anomaly contour map. A magnetic

anomaly map was interpreted in order to obtain additional

geological information at depths by using available surface

geological information as constrains. These constrains are

geological maps and rock outcrops in the study area for

making a model using the Encom Model VisionPro v7.0

software.

For the gravity surveys data were collected using a

LaCoste and Romberg Model G gravimeter with 101 stations

along road traverses with a station spacing of 2 to 3 km.

The base station was at Wat Bang Pueng, which is also a

benchmark for the elevation, and it was calibrated with PSU

absolute gravity station at Prince of Songkla University in

Hat Yai, Songkhla Province. The observed gravity readings

obtained from the gravity survey must be corrected for all

known gravitational effects not related to the subsurface

density changes, including latitudinal variations, elevation

changes, topographic changes and earth tides (Parasnis,

1997). The interpretation of Bouguer gravity anomaly

usually involves separating a residual gravity anomaly due

to an object of interest from some sort of regional gravity

field. The gravity anomaly of the study area was plotted as

Bouguer anomaly contour map. Bouguer gravity anomaly

maps were interpreted in order to obtain addition geological

information at depths by using available surface geological

information as constrains. These constrains are geological

maps and rock outcrops in the study area for making a model

using VisionPro v7.0 software.

RESULTS

Airborne magnetic data and interpretation

The measured total magnetic field map of the study area

is shown in Figure 5a which has values between 40,800 to

41,260 nT. The International Geomagnetic Reference Field

map (IGRF), version 1985 where was downloaded from

http://wdc.kugi.-kyoto-u.ac.jp/igrf/point/index.html has val-

ues between 41,078 to 41,097 nT for the same area as shown

in Figure 5b that was subtracted from the total magnetic field.

The resulting magnetic anomaly map has values between -

300 to 159 nT (Figure 5c). The magnetic anomalies were

correlated with the near surface geology in the study area

using the geological map from the Department of Mineral

Resources. The northwest, central and southeastern part

of the study area have clearly very low and very high

magnetic anomalies that vary from -283 nT to 159 nT that

can be related to intrusive bodies. In the northwestern

part a granite body can be related to a magnetic anomaly

that varies from -283 to 159 nT. In the central area the

location of rhyolite and syenite rocks correlate with magnetic

anomalies that vary from -206 to 140 nT. In the southeast area

a granite rock relates to the magnetic anomaly that varies



from -240 to -9 nT. The results of automated techniques

including the horizontal gradient magnitude method (HGM)

and the analytic signal method (AS) presented the locations

of magnetic contacts zones and depths of magnetic source

shown in Figure 6. Interpretations from these methods show

three areas of magnetic sources. The first area, its depth and

boundary of magnetic contacts are approximate 500 to 2,000

m at UTM 487000 E - 500000 E and 890800 N - 920000 N.

The second area is approximately at 500 to 1,200 m depth

and at UTM 508000E - 512000E and 890000N - 899000N.

The third area is approximately at 500 to 2,300 m depth and

at UTM 508000E - 512000E and 890000N - 899000N.

The Euler deconvolution interpretation with SI = 0, SI

= 1, SI = 2 and SI = 3 (SI = 0 indicates a magnetic contact

zone, SI = 1 indicates a boundaries of sills, SI = 2 indicates

a pipe or horizontal cylinder body, and SI = 3 indicates a

spherical body shape) shown in Figure 7. The results of the

estimated depths from Euler deconvolution method present

different depths at each SI value. The estimate depth for SI

1 provides values that are close to the ones from the HGM

and AS method (Figure 6e) while the estimated depths for SI

0 show lower values and SI 2 and SI 3 have higher values.

Therefore, the estimated depth for SI 1 likely corresponds

with the geological structures of the study area. Note, at each

SI and window size the method cannot estimate the depths of

the magnetic anomalies in the south eastern part of the study

area. This might be because the magnetic bodies have a small

diameter and because of a limited window size in the Oasis

Montaj Viewer software used for the Euler deconvolution.

Subsurface geological model

The interpretation of the magnetic anomaly has been per-

formed along three selected profiles through 2.0D modeling

(Figure 5c) The magnetic susceptibility values that were

determined in the laboratory were relatively low for an

igneous rock in the study area so a value was used which

can best fit the curve of the magnetic anomaly when the

depth of magnetic body fits the boundary conditions in the

study area. At the profile AA’ a low magnetic anomaly of

about -249 nT is at 914500 N position with the magnetic

susceptibility value used for the granite rock is 0.0300000

SI for a magnetic body at 2 km depth. The modeling misfit

rms error was 3.133 %. The magnetic body is similar to

the shape of a vertical cylinder (Figure 8a). A low magnetic

anomaly of about -148 nT can be found at 893000 N position.

A magnetic susceptibility value of the syenite rock there used

was 0.0120000 SI for a magnetic body of 1 km depth. The

modeling misfit rms error was 2.982 %. The magnetic body

is similar to a vertical cylinder shape (Figure 8b). A low

magnetic anomaly of -230 nT at 870000N position can be

found. For this location there is no rock sample available, but

on the geological map a granite rock can be seen. Therefore,

a magnetic susceptibility value of 0.0125000 SI was used for

the magnetic anomaly having a magnetic body at 2 km depth.

The modeling misfit rms error was 3.818 %. The magnetic

body is similar to a triangular prism shape (Figure 8c). Using

all the information from the 2D modeling a 3D model was

created that shows that the magnetic bodies are distributed

from north to south in the study area with differences in

depth and magnetic susceptibility as shown in Figure 5d. The

3D perspective view of the magnetic bodies along the three

profiles in the study area is used for visualization (Figure 8d).

GRAVITY DATA AND INTERPRETATION

The Bouguer gravity anomaly map of the study area is shown

in Figure 9. The main part of the study area has very low

Bouguer anomaly values of -80 to 10 g.u. correlated with

Quaternary and Tertiary sediments, mid Bouguer anomaly

values of 40 to 65 g.u. in the northwestern part that can

be related to granite outcrops of Cretaceous Period and very

high Bouguer anomaly values of 80 to 130 g.u. in the

eastern to southeastern part of the study area that can be

related to sandstone of Cretaceous-Jurassic-Triassic Period

and limestone and dolomite of Permian Period as shown

on the geological map from the Department of Mineral

Resources. The interpretation of the Bouguer anomaly has

been performed along eight selected profiles through 2.0D

modeling (Figure 9) The first body corresponds to the Qua-

ternary sedimentary filling of the plain with a density of 2.10

g/cm3 including overlying the Tertiary basin (2.30 g/cm3)

that is filled by Krabi Formation. The Permian limestone was

assigned as a basement rock with a density of 2.64 kg/m3 and

the Permo-Carboniferous rocks with 2.55 g/cm3 density that

occur in between Permian limestone. The sedimentary rocks

of Triassic-Jurassic-Cretaceous Period with density values of

2.52 g/cm3, 2.47 g/cm3, and 2.35 g/cm3, respectively, overlie

the basement rock. The igneous intrusive body, syenite

porphyry, has a density of 2.90 g/cm3.

SUBSURFACE GEOLOGICAL MODEL

The Bouguer anomaly of Profile AA’, BB’, and CC’ (Figure

10a, b and c) is low in the western part (486500 E to 489200

E) and the central part (494500 E to 498000 E) shows a

syncline shape correlated with Quaternary sediments and

Tertiary sedimentary rock and the Bouguer anomaly high

also shows an anticline shape that can be correlated to the

Permo-Carboniferous and Permian limestone. The central

part of Profile BB and CC present horst and graben struc-

tures; on Profile BB’ the hot spring KB1 have a manifestation

at 499329 E and 900071 N near a cannel in a rubber garden,

which is located at boundary of the horst structure of the

Permian limestone in the 2D geological model. The misfit

of the gravity modeling in profile AA’, BB’, and CC’ were

1.582%, 0.933%, and 1.092% rms error, respectively.

The Bouguer anomaly of Profile CC’, DD’, EE’, and FF’

(Figure 10c, d, e and f) is low in the central part (497000



Figure 5 Aeromagnetic map of the study area; (a) magnetic contour map of study area, (b) IGRF contour map of the study area, and (c)

magnetic anomaly contour map of the study area, black lines - aeromagnetic cross section of AA’, BB’, and CC’. Grid in UTM based on

WGS-84.

Figure 6 Results of automated techniques including the HGM, and AS: (a) HGM contour map in nT/m of the study area, (b) AS contour

map in nT/m of the study area, (c) estimate depths in meter from HGM method overlain on the horizontal gradient magnitude contour map,

and (d) estimate depth from AS method overlain on the analytic signal contour map. Grid in UTM based on WGS-84.

E to 515000 E) shows a syncline shape correlated with

Quaternary sediments and Tertiary sedimentary rocks and

the Bouguer anomaly high also shows an anticline shape in

the most eastern part that can be correlated to the Permian

limestone and Triassic-Jurassic rocks; there the horst and

graben structures are modeled with characteristics of fault

block structures. In the Profile CC’ and DD’ at 511500 E to

514000 E folding structures of the formation are modeled at

the location of a syenite intrusion. Moreover, in the Profile

FF’ in the most eastern part appeared the dolomite with a

density of 2.70 g/cm3. In addition, the hot spring KB2 is

located on profile DD’ (499829 E and 892055 N) near the

cannel and in the 2D geological model it is located at the

boundary of the horst structure of the Permian limestone.



Figure 7 Results of the estimated depths from Euler deconvolution method where position and depth of magnetic overlain on the magnetic

anomaly contour map; (a) SI 0 and WS 15, (b) SI 1 and WS 17, (c) SI 2 and WS 17, (d) SI 3 and WS 25, (e) estimate depth from HGM,

AS, and Euler deconvolution method. Grid in UTM based on WGS-84; SI - structural index, WS - window size.

Figure 8 2.0D modeling of the magnetic anomaly interpretation has been performed along 3 selected profiles; (a) magnetic model along

profile AA’, (b) Magnetic model along profile BB’, (c) Magnetic model along profile CC’, and (d) 3D perspective of magnetic body of three

profile in the study area with azimuth 309 and inclination 20; Horizontal axis in UTM based on WGS-84, vertical scale in m. Top section

is a residual magnetic line which is the straight line when rms error is zero, Middle section shown three lines; black line is the magnetic

anomaly data, pink line is the regional magnetic and red line is the magnetic model.



Figure 9 Bouguer gravity anomaly contour map of study area;

black lines - gravity cross sections of AA’, BB’, CC’, DD’, EE’,

FF’, GG’, and SS’.

The hot spring KB3 located on profile EE’ (509958 E

and 888525 N) is at the boundary of a horst and graben

structure of Jurassic-Cretaceous rocks in the 2D geological

model. The misfit of the gravity modeling in profile DD’,

EE’, and FF’ were 1.389%, 0.906%, and 0.697% rms error,

respectively.

The Bouguer anomaly values of Profile GG’ (Figure

10g) increase from the western part towards the eastern one.

In this section, hot spring KB5 is located in a hot cannel

at 522572 E and 876715 N, which is also well known as

Sapan Yung hot waterfall, located near a proposed fault line

in the 2D geological model. The misfit of gravity modeling

in Profile GG’ has an rms error of 1.297%.

The Bouguer anomaly of Profile SS’ (Figure 10h) is low

in the southern part (885000 N to 889000 N) and shows a

syncline shape correlated to the Tertiary sedimentary rock

and a Bouguer anomaly high (890000 N to 913000 N) also

shows an anticline shape that can be correlated to large folded

structures of Cretaceous-Jurassic rocks and Permian rocks

possibly from the syenite intrusion. In the central part of

profile a basin structure is present. The misfit of the gravity

modeling in profile SS’ has an rms error of 1.305%.


According to aeromagnetic model the magnetic anomaly

changes from -100 nT to 50 nT in the middle of profile BB’

related to the occurrence of syenite rocks that are exposed

at the surface with an estimated depth of 1 km from HGM,

AS, and Euler deconvolution method. This is in agreement

with the gravity profile SS’ presenting the syenite rock at

near surface at the same location as in the magnetic model.

Moreover, the gravity profiles of this study were compared

with seismic sections from lignite exploration in 1982. The

two gravity models in Profile CC’ and EE’ were compared

with two cross sections from seismic survey nearby, Line 4

and Line 6, respectively (Leow, 1985). It has to be noted

that the absolute depth values of both section to be compared

as shown below are quite different as the gravity modeling

reaches down to larger depths. For the stratigraphic cross

section Line 4 of the seismic surveys was compared with the

gravity Profile CC’ (Figure 11a) where the depth estimates

for the Tertiary sediments are with 200 to 300 m similar

for both sections, while Profile EE’ was compared with the

stratigraphic cross section Line 6 from seismic survey and the

stratigraphic geological section from borehole (Figure 11b

and c). Both section profiles show a remarkable agreement,

especially in the western part with the horst and graben

structures and the Tertiary sediment thickness is about 300

to 600 m. In the eastern part the situation is more complex as

the graben boundary structure is not clearly reflected in the

stratigraphic cross section of the gravity model. However, the

resolution of the seismic section is better due to the spacing

of the data in the gravity survey and the gravity methods

itself.

The comparison of the stratigraphic cross section from

seismic surveys with the gravity models shows that the

main structures, here mainly horst and graben structures,

can be revealed by the gravity survey, especially that the

data spacing is sufficient enough. In the geological cross

section and seismic section Line 6, which were compared

with gravity section Profile EE’, show a changing of the basin

with dip from east to west. For the comparison shown here it

has to be noted that the seismic data are from the early 1980s

and that therefore the quality in acquisition, processing and

interpretation might differ from present data if available.

The Tertiary basin shows that the fault block structures

were related to several minor faults in NW-SE and NE-SW

direction (Figure 12). The location and strike of the main

subsurface faults drawn from the 2D gravity models present-

ing horst and graben structures reveal normal faults mainly

in the west and northwest and fault block structures in the

southeast with a remarkable agreement with the contour map

of minor faults in the Krabi basin from lignite exploration in

1982 (Leow, 1985). The hot springs are mainly located at

the border position of horst and graben structures with KB1

(499329 E 900071 N), KB2 (499829 E 892055 N) and KB3

(509958E 888525N). All of hot spring surface locations are

covered by 150 to 300 m thick layers of Tertiary sediments.

Hot spring KB3 is located at the Nattha Waree Hotspring

Resort and Spa, a private commercial area with some public

space. A borehole was drilling showing at around 20 m depth

hot water of 55 °C flowing. In the area near the well hot water



Figure 10 Models of Bouguer anomaly of eight profiles selected; (a) along profile AA, (b) along profile BB’, (c) along profile CC’, (d)

along profile DD’, (e) along profile EE’, (f) along profile FF’, (g) along profile GG’, and (h) gravity model along profile; Horizontal axis in

UTM based on WGS-84, vertical scale in m. Top section is a residual gravity line which is the straight line when rms error is zero. Middle

section shown three lines; black line is the gravity anomaly data, pink line is the regional gravity, and blue line is the gravity model.

was also seeping naturally at several places out from the

ground. However, another borehole about 30 meters further

to the east with drilling depth of about 20 m shows cool water.

This shows that hot water outflow can be quite localized. A

possible reason for that is that some faults might be extended

to shallower depth and by this either channeling the hot

water flow up to close to the surface, or these faults acting

as boundaries in the shallow groundwater system, dividing

areas of hot water and normal cooler groundwater.

A schematic geothermal system for Krabi Province is

shown in Figure 13, which shows that the system is clearly

associated with the horst and graben structures (KB3). The

normal faults are the pathways for the cold meteoric water

from the recharge area and also for the hot water to the

discharge areas, making it a circulating flow path of the

geothermal water. Hot spring KB4 (512317 E 873646 N)

is brine (saline) hot water which covers an area in the

mangroves in the western part, also shown schematically in

Figure 13.

The aeromagnetic interpretation and gravity surveys and

geological investigations were carried out in the geothermal

area in Krabi Province in order to understand the geological

structures related to the geothermal system and the possible

pathways of the hot spring waters as well as heat source

related to the geothermal system.

The gravity measurements and modeling results show

fault block structures, which are related to several minor

faults in mainly N-S strike direction (NW-SE, NE-SW, and



Figure 11 Gravity models compared with the stratigraphic cross sections from the seismic survey; (a) gravity model in Profile CC’ (top)

with the cross section Line 4 from seismic survey (bottom), (b) gravity model in Profile EE’ (top) with geological cross section from

borehole (bottom), and (c) gravity model in Profile EE’ (top) with the cross section Line 6 from seismic survey (bottom); blue line -

boundary of basin and horst show a remarkable agreement. Red line - basin from the cross section Line 6 shows a narrow basin that cannot

be seen in the gravity model.

Figure 12 Contour map of Tertiary sediment depth in meters of Krabi basin from 2D gravity model. Pink star are the hot spring locations,

the white lines are the normal fault locations indicated from Tertiary sediment depths, and the red lines are the normal fault locations that

separated the sub basins drawn from the figure on the right from the lignite exploration in the 1980s (Leow, 1985).

N-S). These horst and graben structure are in agreement with

earlier investigations in the 1980s during lignite exploration

in this area, mainly based on reflection seismic and borehole

data. The structures are the result of extensional tectonics in

Tertiary time. The thickness of the Tertiary sediments from

this and the earlier study are also in good agreement.

The normal faults related to the horst and graben struc-

tures are likely the pathways of the geothermal water from

deeper subsurface to the surface, as well as for the colder

meteoric waters to recharge the hot water system. Higher

salinity of some of the hot spring water (e.g. KB4) in-

dicate that the geothermal system is actively connected,

likely through subsurface faults, to the Andaman Sea further

south and west concentrations (Na ≈428-2,783 mg/L and Cl

≈1,682 mg/L).

Igneous bodies were found partly with surface outcrop

and distributed from north to south with a depth of 2 km in

the south eastern part and the north western part, and at a



Figure 13 Integrated and schematic geological cross-section of the study area of the geothermal system along a west-east profile. T - the

Tertiary sedimentary rock, JKl - Lam Thap Formation of Jurassic-Cretaceous Period, JK - Khlong Min Formation of Jurassic Period, TRl -

Triassic rock, P - Permian limestone, and Sy - Syenite rock.

depth of 1 km in the central part of study area. It is possible

that they act like a local heat source, for example of hot

spring KB3. However, it is unlikely, due to the relatively

small size of these igneous bodies inferred from gravity

modeling, that they are the heat sources for the whole Krabi

geothermal system. The heat source might be at further depth

related to a higher heat flow due to the extensional tectonics;

however no data are available. Another possibility is that the

heat source is located outside the study area and that the fault

system provides the pathway for the hot fluids into the study

area. A possible area for the heat sources might be in the

Khlong Marui Fault Zone as indicated by previous studies

(see Watkinson et al., 2008). Future work might look further

into these possibilities.

ACKNOWLEDGEMENTS

This research has been supported by the Graduate School,

Prince of Songkla University, PSU, Thailand, which is highly

acknowledged. Thanks also to the International Program

in Physical Sciences of Uppsala University, Sweden, for

supporting research equipment and interpretation software.

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University.


Geomechanical Simulation of Deformation by CO2

Injection into Homogeneous Sandstone

Avirut Puttiwongraka,∗, Toshifumi Matsuokaa

a Environment and Resource System Engineering Laboratory, Department of Urban Management, Graduate School of

Engineering, Kyoto University-Katsura, Nishikyo-ku, Kyoto, 615-8540, JAPAN∗, E-mail: [email protected]

ABSTRACT

Geomechanics has become increasingly important because more and more new projects involve viscous or immobile oils, higher

temperatures, pressures and depths, and reservoir materials that are weak, intensely fractured, or highly compressible. Deformation

measurements are critically important for geomechanics. The geomechanical processes are due to injection of CO2 in CCS (Carbon

dioxide Capture and Storage) project, i.e. pressurization, causes expansion of reservoir that may result in a ground surface deformation.

In this study we analyzed and re-produced deformation results of Berea sandstone core sample, which is well known in testing to be

homogeneous and isotropic material, from laboratory experiment using geomechanical simulation. The experiment was setup to emulate a

surface uplift problem caused by CO2 injection in CCS site. Strain changing of core sample in laboratory testing can be implied to reservoir

deformation in field observation. The experiment was divided into three stages in order to measure the strain, i.e., (1) Confining pressure

change stage, (2) Water injection stage (Pore pressure changes) and (3) CO2 injection stage, with continuous tests, respectively. We used

FLAC3D simulator with coupled fluid-mechanic interaction process to simulate core sample deformation based on stages of laboratory

experiment. The simulation results are discussed and proposed that effective porosity changes (closing/opening effect of pore connection)

can explain strain changes during both caused by confining pressure increase (closing of pore connection) and water injection (opening

of pore connection). In addition, we applied this theory to simulate in the case of CO2 injection stage. The deformation of core sample

caused by CO2 injection can be also clarified by change of effective porosity relating to bulk modulus. Finally, the simulation can help us

to understand the deformations of Berea sandstone on three stages of laboratory testing, especially in the case of CO2 injection in which

relates to CCS project.

KEYWORDS: Geomechanical simulation, CO2 injection, FLAC3D, Rock deformation, Berea sandstone

INTRODUCTION

Nowadays, geomechanics has been extensively interested be-

cause of many hydrocarbon fields are geometrically complex

and irregular, and the rock properties are spatially variable,

such an assessment can be conveniently done on a numerical

model of the field understudy (Orlic, 2008). Petroleum

geomechanics has evolved differently from civil and mining

geomechanics. Petroleum geomechanics is far young and has

followed a direction based far more on physics, analysis and

field observations than on laboratory testing and empirical

models (Dusseault, 2011).

The geomechanics plays a prominent role in the assess-

ment of the impact of CO2 injection on the induced surface

deformation. Because CO2 injection changes the pressure

in the reservoir, it also affects the state of stress within the

reservoir and surrounding rocks, the pressurization causes

vertical expansion of the reservoir and changes in the stress

field. These changes are proportional to the magnitude

of the pressure increase and depend on the geometry and

geomechanical properties of the reservoir and surrounding

sediments. The vertical expansion of the reservoir may result

in a ground surface deformation (Rutqvist, 2012).

In this study we used geomechanical simulation to ana-

lyze and re-produce laboratory results of core sample in term

of deformations. Strain changes of core sample in laboratory

testing can be inferred to reservoir deformation relating to

a surface uplift in field observation. We used FLAC3D

simulator with a coupled fluid-mechanic interaction feature

to simulate core sample deformations based on stages of

laboratory experiments. We used a relationship of effective

porosity and effective bulk modulus changes proposed by

Russell and Smith (2007) and Gussmann’s equation (Guss-

mann, 1951) to apply for simulations. Consequently, the

simulation results can help to understand the deformations

of core sample in laboratory testing, especially a case of CO2


Geomechanical simulation of deformation by CO2 injection

injection in which it relates to Carbon dioxide Capture and

Storage (CCS) project.

SURFACE UPLIFT CAUSED BY CO2 INJECTION

Rock deformation and stress are important because the injec-

tion of CO2 , in general, produces an increased pore pressure

will change the stress field cause deformations in the rock

mass. Additionally, reactions with minerals require much

longer time scale than the other sequestration mechanisms

(Winterfeld and Wu, 2011), so the rock deformation and

stress change are mainly due to geomechanical processes. By

the same processes, an underground injection of compressed

CO2 can be produced the ground surface to uplift because of

a reduction in effective stress in the formation.

Geomechanics can be used to monitor geomechanical re-

sponses and for detecting subsurface geomechanical changes

and tracking fluid movements. The changes of reservoir

pressure, stress stage and other geomechanical parameters

can be predicted by geomechanical simulation. Therefore,

surface uplift evaluation is normally calculated before injec-

tion of fluid or steaming that it is important for the design of

safe operations compatible with the environment. Recently,

the importance of geomechanics has become more widely

conducted in Carbon dioxide Capture and Storage (CCS)

project to avoid problem, e.g., wellbore stability, hydraulic

fracturing, sand management, subsidence or surface uplift,

etc.

STRAIN MEASUREMENTS IN LABORATORY

TESTING

Laboratory experiment was setup by Horiuchi et al. (2012) to

emulate surface uplift problem in CCS site. Strain changes

of core sample caused by CO2 injection can be inferred to

reservoir deformation relating to surface uplift. A main ob-

jective in this experiment is to try a capture of the motion of

CO2 front in the rock sample by monitoring the strain of the

sample using optical fiber and strain gauge. A core sample

of Berea sandstone was selected as an ideal homogenous and

isotropic reservoir. In addition, hydrostatic loading, which

is the conventional laboratory test procedure followed by the

petroleum industry, was used as external pressure condition

in this testing. To avoid a rock deformation of thermal

effect, temperature was kept constant at 40°C throughout

testing procedures. The experiment was separated into three

stages, i.e., confining pressure change stage, water injection

(pore pressure change) stage and CO2 injection stage, with

continual tests, respectively. Strains were measured at each

stage by strain gauge and optical fiber. The locations and

directions of strain measurements are shown in Figure 1 and

a schematic of three stage experiments is shown in Figure 2.

Confining pressure change stage

The confining pressure exerted on a core sample with hy-

drostatic loading condition. Oil was injected from a syringe

Figure 1 Locations and directions of strain measurements both

strain gauge and fiber optic.

Figure 2 Schematic of three stages in the laboratory experiment.

pump into a pressure vessel to control and change confining

pressure (Figure 2). For protecting oil penetrates to the

core sample, the sample was covered by silicone before it

was taken inside the pressure vessel. At this stage, rock

sample was in dry condition, only confining pressure, was

increased from 2 MPa to 4, 6, 8, 10, and 12 MPa, induced

strain changes. Finally, strain was measured at each step of

confining pressure changes.

Water injection (pore pressure change) stage

After confining pressure was kept constant at 12 MPa, water

was injected into the core sample from water syringe pump

until the pore pressure reaches the pre-specified values. The

injection pressures of water were from 2 MPa to 4, 6, 8,

and 10 MPa. Strain at each step of injection pressures were

considered to be strains at each state of pore pressures, so we


Puttiwongrak and Matsuoka

can conclude that strain changes in this stage are caused by

pore pressure increases.

CO2 injection stage

At conditions of 12 MPa confining pressure and 10 MPa pore

pressure, CO2 in supercritical state was injected from CO2

syringe pump into the core sample at a bottom. The injection

pressure of CO2 is 10.05 MPa. During CO2 was injected, the

existing fluid was expelled at a top of the sample through a

water syringe pump. Strain was measured corresponding to

elapsed time of CO2 flows inside the core.

DESCRIPTION OF SIMULATION

FLAC3D is used as a simulator in this study. It is a numerical

modeling based on Finite Difference Method (FDM) codes

for advanced analysis of soil, rock, and structural support in

three dimensions and it can model complex behaviors which

do not readily suit for Finite Element Method (FEM) codes,

e.g., large displacements, non-linear strain, unstable systems,

and problems that consist of several stages. According to a

coupled fluid-mechanic interaction feature it is satisfactorily

able to simulate stages of fluid injection problems.

Rock deformation model

At the 1st stage of simulation (confining pressure change

stage) rock is in a dry condition, thus only mechanical

processes are operated in this stage. Rock deformations or

strain (ǫ) are due to confining pressure (Pc) and dry bulk

modulus of rock (Kd) as followed:

ǫ =Pc

Kd

(1)

The calculating strain in FLAC3D is derived from nodal

velocities, as usual. The strain rate (ǫij) is then partitioned

into deviatoric, eij , and volumetric strain, ǫv , components:

ǫij = eij + ǫvδij (2)

where deltaij is the Kroenecker delta.

A nodal volumetric strain is calculated using the for-

mula:

ǫv,n =

mn∑

e=1ǫv,eVe

mn∑

e=1Ve

(3)

where mn are the elements surrounding node n, and Ve is the

volume of element e.

In cases of fluids (water and CO2 ) injection stages, the

coupled fluid flow and mechanical simulations, as known

coupled fluid-mechanic interaction feature in the FLAC3D

software, are operated. Strain changes with time are corre-

sponding to fluid flow and mechanical changes. Changes in

the variation of fluid content, ǫ, are related to change in pore

pressure, Pp, saturation, S, and mechanical volumetric strain,

ǫv . The response equation for the pore fluid is formulated as

1

M

∂Pp

∂t+

φ

S

∂S

∂t=

1

S

∂ξ

∂t− α

∂ǫv∂t

(4)

where M is Biot modulus, φ is the porosity, α is Biot

coefficient.

During confining pressure increases in the first stage, the

density of the rock increases and lead to dry bulk modulus

increases because of grain contacts in the rock framework

following compaction theory. According to grain contacts

together in the rock framework, it influences the pore con-

nections are closed, and then the effective porosity decrease.

Similarly, when the fluid is injected into rock, the injection

pressure pushes the pore connection up to open again. Hence,

the effective porosity increases during steps of pore pressure

increases. Because a rather insensitive stress-permeability

relationship for sandstones (Rutqvist and Tsang, 2003), thus

the changes in permeability are omitted in this study. The

relationship of change of effective porosity (φe) and dry bulk

modulus of rock, which finally influences on strain change,

can be explained by an equation proposed by Russell and

Smith (2007) as expressed:

Kd = Km

(

1

1 + φe

k

)

(5)

where Km is the matrix bulk modulus (In this study we used

matrix bulk modulus of quartz, 36 GPa, represents matrix

bulk modulus of sandstone) and k is the pore space stiffness

over matrix bulk modulus ratio. The value of k can be

determined by a graph as shown in Figure 3.

Figure 3 A modulus of dry rock over matrix bulk modulus ratio

curves for varying values of k (Russell and Smith, 2007).



Table 1 Physical properties of Berea sandstone.

Parameter Value

Dry bulk modulus (GPa) 8.3

Shear modulus (GPa) 7.0

Dry density (kg/m3) 2100

Permeability (mD) 100

(Initial) Porosity (%) 23

Furthermore, Gassmann’s equation was used to convert

the time-dependent pore pressure and saturations resulting

from the flow simulation into elastic rock parameter changes

(Gassmann, 1951). Gassman’s equation computes effective

bulk moduli of rock saturated (Ksat) with a given composi-

tion of pore fluid from the elastic moduli of the dry rock. The

equation is

Ksat

Km −Ksat

=Kd

Km −Kd

+Kf

φe(KM −Kf )(6)

where Kf is the total fluid bulk modulus which can be

calculated by

1

Kf

=Sw

Kw

+SCO2

KCO2

(7)

where Sw and SCO2are the saturation of water and CO2

and Kw and KCO2are the bulk modulus of water and CO2 ,

respectively.

Model setup

Model geometry was created corresponding to the core

sample in the laboratory. The size of core sample is 5 cm

of diameter and 10 cm of length. The simulation problem

was discretized into 3-dimensional mesh, the mesh size in x,

y, and z direction is 16×16×32 as shown in Figure 4. The

calculating volumetric strains are consistent with the same

location and direction of strain measurements of optical fiber

(Figure 1) in the laboratory. The problems of our simulations

are in nonsupport system, thus the confining pressure with

hydrostatic loading condition is performed as mechanical

boundary condition and elastic model was for simulations.

Rock properties of Berea sandstone that were used to be

input parameters are standard properties of Berea sandstone

as shown in Table 1.

After we setup model geometry, boundary condition,

and input parameters, the model was examined until a result

is satisfactory and then the three stages of experiments in

laboratory were simulated in order to calculate volumetric

strains, respectively. Fluid bulk modulus of water and CO2

in supercritical phase which were used in this simulation are

1 GPa and 0.05 GPa. For mixing fluid bulk modulus of water

and CO2 can be determined by (7) with assuming saturation

of water and CO2 are 0.3 and 0.7, respectively.

Figure 4 Setup of model geometry and the locations of simulation

strains.

DISCUSSIONS AND RESULTS OF SIMULATIONS

Here will be demonstrated simulation results of each stage,

i.e., (1) confining pressure change stage, (2) water injection

stage, and (3) CO2 injection stage, which are constrained by

experimental results in the laboratory testing. The simulation

results are discussed to explain suspicions of experimental

results and help to understand a mechanism of deformations

of porous material caused by fluid injection.

Simulation result vs. experimental result

In order to compare simulation and experimental results, we

need to convert a volumetric strain from simulation results

to experimental strain (ǫexpt.) in the direction which was

measured by optical fiber.

As we can determine an angle (θ) between optical fiber

direction and x-axis as shown in Figure 6, and then a

definition of volumetric strain of cylinder material can be

expressed by (8). Hence, we can convert volumetric strain

to experimental strain in the same direction of optical fiber

by using (9). Those equations which we used for conversion

are

ǫv = 2ǫd + ǫl (8)

ǫexpt. =ǫv

2 cos θ + sin θ(9)



Figure 5 Workflow of simulations.

where ǫd and ǫl are the diametrical strain and longitudinal

strain, respectively.

Simulation of confining pressure change stage

Both simulation and experimental results of confining pres-

sure change stage demonstrated that the strain deceases

were due to confining pressure increases (Figure 7). The

simulation results seem to be consistent with experimental

results and almost straight line. We simulated deformations

of rock in this stage using dry bulk modulus of rock as input

parameters as shown in Table 1 for every confining pressure,

thus Figure 7 shows that during confining pressure increases

with hydrostatic loading condition, density of rock is in-

creased influencing the dry bulk modulus increase because

grain contacts, as followed compaction theory, or effective

porosity decrease because pore connection closing is less

sensitive, so the graph shows straight line and can be simu-

lated by only one value of Kd throughout confining pressure

changes. The rock behaved deformations as a shrinkage in

Figure 6 A schematic of volumetric strain-optical fiber conversion.

Figure 7 Comparison between simulation and experimental results

of confining pressure change stage.

this stage. In addition, variations of strain measurement of

each location in laboratory when confining pressure increases

can be explained by indirect confining pressure exerted on the

sample and the core (itself) is not completely homogeneous,

but the simulation model is perfect homogeneous model.

As the confining pressure is controlled by oil injection, the

silicone was used to cover the rock to protect oil penetration;

therefore, the sample is probably not equal at each location

because differences of silicone thickness.



Table 2 Information of injection volume of water, effective poros-

ity, and modifications of effective bulk modulus of rock.

Pp (MPa) VH2O (ml) φ(%) =VH2O

V ∗

rockKH2O−sat(GPa)

2 28.45 14.5 12.62

4 34.02 17.3 11.34

6 35.20 17.9 11.00

8 35.93 18.3 10.83

10 36.65 18.8 10.65

*Rock volume, Vrock = 196.35 cm3

Simulation of water injection (pore pressure change) stage

In water injection stage, after confining pressure was kept

constant at 12 MPa, the water was injected to increase pore

pressure with injection pressure increases from 2 MPa to 4,

6, 8, and 10 MPa. We simulated and calculated strains at

saturated stage of each injection pressure of water injection.

The simulation results show two cases of simulation, i.e.,

at first case we simulated and calculated strains without

determination of effective porosity changes, and second case

was simulated and calculated strains with applying effective

porosity changes. As we mentioned above, changes of

effective porosity caused by pore connection closing or

opening affects changes of dry bulk modulus, and then causes

changes of effective bulk modulus of rock saturated with

water. Moreover, information of injection volume of water

from laboratory testing assuredly indicated that at confining

pressure is 12 MPa, the effective porosity of the core sample

reduced from 23% and the effective porosity continually

increases with increasing water injection pressures. The

concept of effective porosity was adopted in order to evaluate

bulk modulus of water saturated core sample, and effective

porosity can be considered as the multiplication of porosity

of core and water saturation at each pore pressure. Con-

sequently, we used (5) and (6) to determine modifications

of dry bulk modulus and effective bulk modulus of rock

saturated with water because effective porosity increases as

updated input parameters as shown in Table 2.

Figure 8 shows that the simulation results without ef-

fective porosity change are totally different with experimen-

tal results. However, when we applied effective porosity

changes to the simulation, the simulation results here were

more consistent with experimental results. Although, both

results of simulation and experimental results are close

together when we applied effective porosity changes to the

simulation, they are still some differences probably caused

by changes of fluid bulk modulus because changes of pore

pressure in the formation.

Figure 8 Comparison between simulation and experimental results

of water injection stage.

Stress (effective stress)-strain curve of confining pressure

change and water injection stages

Figure 9 shows plots of experimental results both confining

pressure change and water injection stages from laboratory

testing on stress-strain curve. As rock was a dry condition

in confining pressure change stage, thus effective stress

increases were due to confining pressure increases. While,

in water injection stage, we kept a constant of confining

pressure at 12 MPa, thereby the effective stress decreased

because of pore pressure increases.

Rock deformation is based on the concepts of hydrostatic

and linear poro-elasticity theories, rock permeability, poros-

ity, bulk modulus, pore pressure, and confining pressure are

main parameters to play a significant role in this mechanism.

At first stage strain decreased because of effective stress

increases (confining pressure increases), the curve shows al-

most a straight line that is consistent with theory of elasticity,

while strain increased due to effective stress decreases (pore

pressure increase) in the second stage. However, changing

strains of second stage are less than the first stage because

of bulk modulus increases which were caused by rock was

saturated with water. According to a rate of effective stress

changes was constant and permeability change was negligi-

ble, thus a non-linear stress-strain curve of water injection

stage (Figure 9) can be explained by two parameters, i.e.,

porosity and bulk modulus, that it is in accord with applying



Figure 9 Stress (effective stress) âAS strain curve of confining

pressure change and water injection stages.

Table 3 Input parameters for simulation of CO2 injection stage.

PCO2−inj(MPa) φCO2(%) KH2O+CO2−sat (GPa)

10.05 4.2 20.06

an effective stress change to simulations as we discussed in

previous section.

Simulation of CO2 injection stage

A suspicion of strain changes in this stage is a high strain

increment with elapsed time despite a small rate of pore

pressure increase (0.05 MPa). This increased strain seems

to be equal to an increase of strain at 7 MPa of pore pressure

in water injection stage.

A rate of pore pressure increase with 0.05 MPa is not

certainly possible to generate a rate of strain increment in the

laboratory testing for CO2 injection stage. Consequently, we

considered that the high rate of strain increment of this stage

is due CO2 migrates to the narrow and small pores where

water cannot reach these pores during water injection stage

because pore connections are closed together when confining

pressure increases.

As we know effective porosity at final stage of water

injection, i.e., the effective porosity at 10 MPa of pore pres-

sure is 18.8% as shown in Table 2, hence pores that will be

occupied by CO2 (φCO2) can be determined by a difference

of initial porosity and effective porosity at 10 MPa of pore

pressure (23 - 18.8 = 4.2%). Afterward, we created a rock

model in order to simulate a rock that has 4.2% of porosity

and effective bulk modulus of rock (KCO2+H2O−sat), which

saturated with mixing fluid (CO2 dissolved in water), is 20.06

GPa as shown in Table 3.

Equation (5) was used to determine a dry bulk modulus

of rock is due to porosity changes to 4.2%, and then the

effective bulk modulus of rock which saturated with mixing

fluid by using (6) and (7). Parameters, i.e., saturation of

water and CO2 is 0.3 and 0.7, were assumed in order to find

fluid bulk modulus of mixing fluid. Finally, input parameters

as shown in Table 3 using for simulation of this stage are

CO2 injection pressure (PCO2−inj), porosity which will be

occupied by CO2 (φCO2), and effective bulk modulus of

rock which saturated with mixing fluid (KCO2+H2O−sat).

Hence, in this stage strain changes were generated from pore

pressure change of 10.05 MPa.

Figure 10 represents volumetric strains changed corre-

sponding to pore pressure changes from simulation results.

This figure is used to confirm that strain change of rock is

in accord with changing pore pressure of rock. In addition,

motion of CO2 front inside rock, which is represented by No.

1-11 of strain increases with elapsed times, can be explained

by Figure 11.

CONCLUSIONS

Geomechanical simulation using FLAC3D simulator with

coupled fluid-mechanic interaction feature was used in this

study to explain rock deformations of laboratory experi-

ments. At the same effective stress, the differences of

strain changes because confining pressure and pore pressure

increases, as shown in Figure 9, were significantly controlled

by effective porosity and effective bulk modulus changes.

Furthermore, the high rate of strain increments in CO2

injection stage can also be explained by changes of effective

porosity and effective bulk modulus. We used information

of injection volume of water from laboratory testing and

equations (5), (6) and (7) to calculate effective porosity and

bulk modulus changes as input parameters as shown in Table

2 and 3 in cases of simulations. Furthermore, strain was

increased corresponding to a flow of CO2 front inside the

core sample during CO2 injection that can be monitored by

a strain of each location (No. 1-11) of measurements and

simulations.

The rock deformation, is due to injection or depletion

of fluid inferring surface uplift and subsidence, has not been

widely understood up-to-date. Geomechanics are used and

discussed for how monitoring of geomechanical responses

is used for detecting subsurface geomechanical changes and

tracking fluid movements. Therefore, in this study we

propose and hope that our simulation results can help to

understand the deformations of Berea sandstone, especially,

for CO2 injection in which it can be inferred on surface uplift

problem of CO2 storage in abandoned oil and gas fields,

where are normally clastic (sandstone) reservoir rock.



Figure 10 Simulation results of volumetric strain changes with time corresponding with pore pressure changes.

Figure 11 Strain changes vs. elapsed time of CO2 injection stage.

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in Proceeding of TOUGH Symposium 2003, pp. 1–9, Berkeley,

California, USA.

Winterfeld, P. & Wu, Y., 2011. Parallel simulation of co2 seques-

tration with rock deformation in saline aquifers, in Proceeding

of SPE Reservoir Simulation Symposium, pp. 1–22, Woodlands,

Texas, USA.


Dating Geological Events using ThermoluminescenceTechnique

Prakrit Noppradita,b,∗, Sommai Changkiana,c, Helmut Durrasta,b

a Geophysics Research Center, Prince of Songkla University, HatYai, Songkhla, 90112, THAILANDb Department of Physics, Faculty of Science, Prince of Songkla University, HatYai, Songkhla, 90112, THAILANDc Department of Science, Faculty of Science and Technology, Prince of Songkla University, Pattani, 94000, THAILAND


ABSTRACT

Thermoluminescence (TL) dating is a suitable approach for dating geological events by sampling quartz or feldspar rich sediments.

Generally, when minerals in sediments are irradiated by natural-ionizing radiation (from radioactive elements in its surrounding

environment), electrons are continuously accumulated in traps. However, these accumulated electrons can be bleached and reset by sunlight.

Therefore, the calculated age is the last time the sampled sediment was bleached by sunlight (not precisely the event time). Many minerals

in sediments are widely applied as a dosimeter to determine its signal, for example, quartz, which is used in this study. Sediment samples

were collected in Surat Thani Province, where different geological events were identified, for example, alluvial sedimentation processes

or fault movements. The accumulated dose (AD) was determined by the additive dose method where the dose from an artificial source is

added to the natural dose in quartz. The environmental ionizing radiation rate, or the dose rate (D), was determined from the activities of

natural radioactive elements using gamma ray spectrometry. The age of the event is calculated by AD proportional to D. In this study, the

calculated TL ages of alluvial sediment sequences were 122-1,900 ka. The upper sediment layer was formed about 122-225 ka ago. The

lower sediment layer was formed around 1,336-1,900 ka ago shown the evidence of moving of the subsurface because of the discontinuity

of a layer.

KEYWORDS: Thermoluminescence dating, Geological events, Sediments

INTRODUCTION

When studying geological events, it is necessary to carefully

analyze the events, for example, by mapping the detailed

structures. However, often it is also important to know

the timing of the geological event and by this to answer

questions, like, when did this happened or when was the fault

movement?

There are many dating techniques available; both relative

dating (compared with known ages) and absolute dating

methods. The thermoluminescence (TL) dating is a type

of absolute dating the commonly applied to date minerals

in the interesting material such as archeological materials

or sediments. Quartz and feldspar are often used in TL

dating, because both minerals have a high abundance in

sediments and can resist weathering better when compared

with other minerals, such as carbonate minerals (Preusser

et al., 2008). The TL dating has been widely applied in

many studies, such as landscape evolution, palaeoclimate,

geohazards, paleoseismology and many others (Preusser et

al., 2008; Fattahi, 2009).

TL is a technique, which is using the accumulated

signals, TL light, in a mineral. The intensity of the TL

signal depends on the environmental ionizing-radiation and

the duration the mineral has received that radiation.

Natural ionizing radiation

Preusser et al. (2008) described that the ionizing radiation

naturally occurs in form of alpha, beta and gamma radiation

and cosmic rays. Moreover, it can be separated into three

types of ionizing radiation; cosmic radiation, external radia-

tion, and internal radiation. The cosmic radiation is radiated

from space that decreased with depth from surface. The

external radiation is radiated from neighboring grains that

are naturally containing radioactive elements, like potassium,

uranium, and thorium. For the internal radiation, it can be

mainly related to beta radiation in potassium feldspar from

potassium-40.


Dating geological events using thermoluminescence

Figure 1 Thermoluminescence processes: (a) mineral received ion-

izing radiation and electrons are trapped and accumulated, (b) after

the crystal is exposed to sunlight or heat it released luminescence

light called thermoluminescence.

Physical background of TL

Preusser et al. (2008) and Fattahi (2009) described the

physical background in detail. Minerals and their valence

band electrons are exposed to ionizing radiation. These

electrons are exited until they contain sufficient energy to

reach the conduction band. Some electrons may become

detached from their parent nuclei in the crystal lattice and

diffuse in the vicinity of defects in the conduction band

and become trapped at the trap (T) level located below the

conduction band. The duration and the intensity of the

radiation increase are proportional to the number of trapped

electrons.

In case the accumulated electrons in the minerals are

exposed to sunlight or heat, they will receive enough energy

to change to the conduction band level again before suddenly

decreasing their energy level to the recombination center

(R), where the energy level is between T and the valence

band. When particles, electrons, decrease their energy, light

is released, which is the thermoluminescence (TL).

In practice, minerals, which are widely used as dosime-

ters and for the dating, are quartz and feldspar. Quartz, which

is commonly found in sediments, can resist weathering and

its properties are relatively good investigated (Preusser et al.,

2009), and therefore is a preferred mineral in TL dating.

However, in some environments, quartz cannot be found.

Then feldspar is chosen to be the dosimeter, because feldspar

can accumulate electrons in a larger amount than quartz.

Feldspar can also be used to date older ages than using

quartz.

Age calculation

The TL dating uses the duration and the level of the natural

radiation to date the age of sediment, respectively, geological

event. The accumulated electrons in the minerals, called

accumulated dose (AD), and the rate of the ionizing radiation

exposed to the minerals, called dose rate (D), are needed for

Figure 2 Geology of Surat Thani Province (Chotikasathien &

Kohpina, 1993).

the calculation as following

Age =AD

D.

The age calculated from this equation can be interpreted that

the last time minerals in sediments were exposed to sunlight.

The sunlight exposure or the end of it can provide evidence

of a geological event occurred in the past.

STUDY AREA

Surat Thani Province in the southern part of Thailand was

selected as the study area. Chotikasathien & Kohpina (1993)

described that the youngest sediments are generally uncon-

solidated and were formed in the Quaternary. The geological

map (Figure 2) indicates various rock types exposed in the

area as possible sources of the sediments in Surat Thani.

Their ages range from Precambrian to Tertiary.

Quaternary sediments in Surat Thani Province are clas-

sified by Chotikasathien & Kohpina (1993), based on envi-

ronment of deposition, into 1. non-marine lithofacies, and

2. coastal lithofacies. These two lithofacies can be further

classified as following. Non-marine lithofacies: 1. Regolith:

Layer of loose, hetero-geneous material covering solid rock.

2. Colluvium: Loose deposits of rock debris accumulated

through the action of gravity at the base of a cliff or slope.

3. Allu-vium: Loose, unconsolidated (not cemented together

into a solid rock) soil or sediments, which has been eroded,


Noppradit et al.

Figure 3 TL sampling with a steel pipe in a trench (photo here

taken at day light).

reshaped by water in some form, and redeposited in a non-

marine setting. Coastal lithofacies: 1. Deltaic sediments:

Sediments that were formed at the mouth of a river, where the

river flows into an ocean, sea, estuary, lake, or reservoir. 2.

Estuarine and intertidal mud flat: Coastal wetlands that were

formed when mud is deposited by tides, rivers, or estuarine

activities. 3. Coastal-barrier sand.

Further, the Khlong Marui Fault Zone (KMFZ) is likely

to be crossing Surat Thani Province in the northern part. The

KMFZ is known in Phang Nga, Krabi, and Phuket Province.

Watkinson et al. (2008) studied the history of KMFZ further

in the west; it shows a strike-slip environment with a mainly

NNE-trending.

METHODOLOGY

Sampling

In this study, trenches and outcrops were used to collect

samples for TL dating. 50 centimeter long cylindrical steel

pipes were used to collect the samples perpendicular to the

trench or outcrop wall at the interesting points during night

time to avoid any light contamination of the samples (Figure

3). Samples were separated into two parts. The first part was

processed in a dark room for measuring the thermolumines-

cence signals and the second one can be processed at normal

light for measuring the dose rate.

TL dating procedure

Mineral separation

In this study quartz was used to date the geological events.

The first part of the sample (under light protection) was

then cleaned with water and washed with 15% HCl for 40

minutes. After that, 48% HF was used for 40 minutes for

removing the sample skin and other contaminated minerals.

Then fluoride ions were removed from the sample using 15%

HCl for 15 minutes. Finally, the sample was cleaned again

with distilled water and then dried.

Accumulated dose determination

A heavy liquid with 2.62 g/cm3 density was prepared using

tetrabromoethane and dipropylene glycol. Then the dry-

clean sample was put into the heavy liquid and centrifuged

at 2,000 rpm for 1 hour. Quartz that has a mineral density

of 2.65 g/cm3 is separated. The sample with quartz only was

cleaned with distilled water and acetone and kept in small

light-protection bags. Then the sample was irradiated with

gamma rays using a Co-60 source at different doses from 0 to

1,400 Gy at the Office of Atom for Peace in Bangkok. For the

measurement of the thermoluminescence of each irradiated

sample a Harshaw 3500L reader was used. The data are

shown in temperature versus TL intensity that are called glow

curve, which are similar to a Gaussian peak curve (see Figure

4). The WinREMS software was used to measure and export

data from the TL reader before analysis the curve by GlowFit,

which was used to calculate an area under the curve. GlowFit

is a freeware developed by the Institute of Nuclear Physics,

Krakow, Poland (Puchalska & Bilski, 2006). The area under

the peak calculated gives the total intensity. Plotting this

with the irradiation dose a linear equation can be drawn.

The x-axis interception (at the total intensity = 0) is called

equivalent dose or accumulated dose (AD). This technique

can be also called additive dose, see Figure 5.

Dose rate determination

For the determination of the dose rate the non-light protection

part of the sample is used. First, the sample is crushed. After

that it is kept in a sealed container for protecting against

radon leakage for a period of one month. Then a gamma

ray spectroscopy is carried out to determine the amount of

U-238, Th-232, and K-40 by using a high-purity germanium

(HPGe) detector at 1.460 MeV for K-40, 1.760MeV for Bi-

214 (U-238) and 2.615 MeV for Tl-208 (Th-232). The three

elements are used for calculating the dose rate (D ).

The specific activity of the sample (concentration of

nuclide) are calculated as following

a(Bq/kg) = kCn

where k = 1/ǫPγMs, a is the specific activity of the sample


Dating geological events using thermoluminescence

Figure 4 Example of a glow curve (Sample TL08) from the TL

reader with temperature versus TL intensity for doses of 0, 200,

400, 800, 1,200, and 1,400 Gy from below to top.

Figure 5 Dose (Gy) versus TL intensity at 325 °C of Sample TL08.

Accumulated dose determination using the additive dose method

gives AD=194.1 Gy.

in Bq/kg, Cn is the count rate at a certain peak, ǫ is a detec-

tor efficiency, Pγ is a number of gammas per disintegration

of this nuclide for a transition at energy E, and Ms is the

mass in kg of the measured sample. The emission of gamma

ray, which is the ionizing radiation of the surrounding earth

material, affects directly the thermoluminescence dosimeter

(quartz or feldspar) over time. The dose rate is the ratio of an

increment dose in a time interval. The dose rate (D) can be

calculated as following:

D(nGy/h) = a(Bq/kg)× CF (nGy/h per Bq/kg),

where a is the concentration of nuclide, and CF is the

conversion factor that 0.429 for U, 0.666 for Th and 0.042

for K (Tsertos & Tzortzis, 2003).

Table 1 Dose rate, accumulated dose, and calculated ages.

Sample (no) Dose rate (Gy/ka) Accumulated dose (Gy) Age (ka)

TL08 0.864 194.1 225

TL09 0.863 1,074.1 1,240

TL10 0.763 1,445.3 1,900

TL11 0.450 55.0 122

TL12 0.437 583.3 1,336

RESULTS

The collected samples are alluvial sediments that have a sand

and clay composition (Figure 6). Several samples were taken

and the age based on the TL method determined. Five of the

samples, their location in a trench and their age are shown

in Figure 5. The TL peaks from the quartz samples mainly

appeared at 153 °C, 210 °C, 275 °C, and 325 °C. The 325 °C

peak is the suitable one for dating quartz minerals (Wintle,

1997); therefore this temperature peak was selected.

The measurement results of Sample TL08 are shown as

an example, with the TL intensity measured and analyzed

intensity for each temperature (see Figure 4 and 5). The

325 °C intensity peak was selected to find the relationship

between its intensity and dose. The x-axis-interception was

determined as its accumulated dose (194.1 Gy). Its dose

rate then can be calculated from the gamma ray spectrom-

etry, which is 0.864 Gy/ka. The age can be calculated to

194.1/0.864=225 ka. For the other samples the results with

dose rate, accumulated dose, and age are shown in Table 1

DISCUSSION

From Figure 6 it can be seen that the L6 layer was formed

about 1,336-1,900 ka ago. The L1 sediment layer on top

was deposited about 122-225 ka ago. The TL09 sample

with an age of 1,240 ka seemingly does not fit in the

other ages related to layer L1. This difference might be

related to various reasons. The sample, for example, might

represent a mixed layer and as a result the TL age might also

show a mixed age. Moreover, high age values often have

high uncertainty, which is related to the saturation of the

trapped electrons. However in this study, the graphs fitted

to determine the AD are looking linear for all samples (not

shown here). Further interpretations of the sedimentological

implications of the TL ages are currently being done.

ACKNOWLEDGMENTS

The authors would like to thank the Development and Pro-

motion of Science and Technology (DPST) Talent Project,

Thailand, and the Electricity Generating Authority of Thai-

land (EGAT) for financial support. Further thanks go to the

people and local government officers in the study area for

supporting this work.


Noppradit et al.

Figure 6 Location of samples in a trench, general lithology, and TL dating results.

REFERENCES

Chotikasathien, W. & Kohpina, P., 1993. Quaternary geology of

coastal area, Surat Thani and vicinity, southern Thailand, Journal

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Index of Authors

Boonyatee, Tirawat, 75

Chaisri, Siriporn, 42, 48, 53

Changkian, Sommai, 138

Chantraprasert, Sarawute, 23, 42, 48

de Wet, Barry, 82

Durrast, Helmut, 117, 138

Ellis, Robert, 82

Giao, Pham Huy, 17, 112

Htike, Soe Linn, 100

Kato, Yoshinori, 75

Kitazumi, Akira, 75

Kluntong, Narin, 68

Kongsuk, Anchalee, 1, 36

Kosuwan, Suwith, 75

Latt, Khin Moh Moh, 112

Limpisawad, Sitirag, 75

Macleod, Ian, 82

Matsuoka, Toshifumi, 129

Mayamae, Aksara, 87

Methaweranon, Wimonsiri, 14

Morio, Satoshi, 75

Munkong, Chatupond, 62

Na Lampang, Tirawut, 36

Nilsuwan, Usa, 117

Ninsom, Chawanun, 42

Noppradit, Prakrit, 138

Norkhamboot, Theerachai, 108

Nuannin, Paiboon, 53, 62

Pananont, Passakorn, 68, 71

Ponchunchoovong, Monkon, 14

Poomvises, Noppadol, 23, 36

Puttiwongrak, Avirut, 129

Rongkhapimonpong, Natthee, 7

Sakulnee, Rapeeporn, 71

Sangtong, Narucha, 36

Sawatdipong, Benjamas, 1, 36

Sommai, Thirat, 31

Somsri, Siriphon, 94

Srisuwan, Preeya, 87

Suanburi, Desell, 7, 10, 14

Suklim, Tanapon, 48

Tadapansawut, Tira, 53

Tansamrit, Songkiert, 10

Tepsut, Boonyoung, 14

Thangkanasup, Channarong, 7

Udphuay, Suwimon, 48

Wattanasen, Kamhaeng, 31

Wiwattanachang, Narongchai, 17

Wongpornchai, Pisanu, 94, 100, 108

Yordkayhun, Sawasdee, 31, 87

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