indian geotechnical conference (igc-2010)igs/ldh/files/igc 2015 pune/theme 5...50th indian...

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INDIAN GEOTECHNICAL CONFERENCE

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DECEMBER 2015, Pune, Maharashtra, India

Venue: College of Engineering (Estd. 1854), Pune, India

DISPERSION ANALYSIS USING ACTIVE MASW SURVEY DATA

Jumrik Taipodia1, Dipjyoti Baglari

2, Shibayan Biswas

3, Arindam Dey

4

ABSTRACT

Multichannel Analysis of Surface waves is one of the recent developments in geophysics which can

characterize the site up to depth of many meters. Site Characterization using MASW involves three steps

i.e. Data acquisition, Dispersion Analysis and Inversion Analysis. This paper explains about the

Dispersion Analysis. Dispersion of waves is the propagation of waves in the different direction, with

different frequency and different phase velocity. In MASW survey an image space is formed which is

basically a 3D plot of frequency and phase-velocity with the energy accumulation at each of the

combination of the former two entities. Based on the relative energy accumulation as observed from the

dispersion image space, the fundamental and the higher order mode dispersion curves can be identified. In

the present study, field experimentation studies have been conducted for active MASW survey using

Sledgehammer (9.82kg) and PEG hammer (40 kg) as the energy source. The dispersive nature of waves

was studied for which samples were collected at different sampling frequencies of 15000Hz, 3750Hz,

500Hz and 50 Hz. It was found out that the resolution of dispersion image space depends upon the

sampling frequency and the input source energy used. The study was carried out using the commercial

softwares EASYMASW and SURFSEIS. Additionally, a MATLAB coding (based on phase-shift method

by Park et al., 1999) is being developed to develop the dispersion image space and extract the dispersion

curve. The coding described here has ability of developing 2D and 3D motion of waves both in time-

domain and frequency domain. Commercial softwares extracts the dispersion curve based on the user

discretion, where the user is required to choose the points for the dispersion curve governed the color

coding of the image space. Such an attempt might result in missing the energy accumulation peaks and

end up with the user selecting a point which should have been actually absent in the dispersion curve. If

selected, this would render incorrect subsurface stratigraphy once the dispersion curve is utilized in an

inversion analysis. Attempt has been made to generate automated curve extraction program from the

image space which thus overcomes the problem faced in the manual picking of the dispersion points.

1J. Taipodia., Assistant Professor, Department of Civil Engineering, NIT Arunachal, [email protected]

2D. Baglari., Assistant Professor, Department of Civil Engineering, JIST Jorhat, [email protected]

3S. Biswas., PG Student, Department of Civil Engineering, IIT Guwahati, [email protected]

4Arindam Dey, Assistant Professor, Indian Institute of Technology Guwahati, [email protected]

mailto:[email protected]




J. Taipodia, D. Baglari, S. Biswas and A. Dey

This paper reports about the dispersion analysis conducted based on the raw records obtained from the

experimental investigation carried out at IIT Guwahati to determine the subsurface stratigraphy at a

particular test site. Active MASW survey was conducted with the application of a sledgehammer (weight

of 10 kg) and a Propelled Energy Generator (PEG of weight 40 kg) as the energy sources to generate

wave-fields across the soil medium. 4.5Hz geophones were used as receivers which provide the output in

the form of voltages generated due to the motion of the elements within the receivers. The offset and

receiver spacing were maintained to be 2 m. Data acquisition involved with the acquisition of

multichannel field records where the raw data was acquired at different sampling frequency from the field

based on different geometrical configurations of the arranged geophones. Based on the collected records,

dispersion analysis was conducted. In this paper, various aspects of dispersion analysis are discussed,

with the emphasis on the extraction of experimental dispersion curve. Two commercially available

software, EasyMASW and SurfSeis have been utilized for the generation of dispersion image, from where

manual picking technique has been used to extract the theoretical dispersion curve. However, since this

technique becomes subjective, an automated dispersion point picking has been developed with the aid of

indigenous Matlab coding which works on the principle of identification of dispersion points having the

highest accumulation of energy during wave propagation.

Keywords: Active MASW survey, Dispersion analysis, EasyMASW, SurfSeis, MatLab coding, Automated

Picking.

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DISPERSION ANALYSIS USING ACTIVE MASW SURVEY DATA

J. Taipodia., Assistant Professor, Department of Civil Engineering, NIT Arunachal, [email protected]

D. Baglari., Assistant Professor, Department of Civil Engineering, JIST Jorhat, [email protected]

S. Biswas., PG Student, Department of Civil Engineering, IIT Guwahati, [email protected]

A. Dey., Assistant Professor, Department of Civil Engineering, IIT Guwahati, [email protected]

ABSTRACT: Multichannel Analysis of Surface Waves is used to define the shear wave velocity profile of soil

strata. Dispersion Analysis is a critical component of the post-processing of data which involves the extraction of

dispersion curve from the dispersion image space. Based on the relative energy accumulation as observed from the

dispersion image space, the fundamental and the higher order mode dispersion curves can be identified. Software

such as EasyMASW and SurfSeis operates on manual picking of dispersion curve which renders the method to be

subjective. This article reports the development of an automated dispersion curve extraction procedure using

Matlab coding.

INTRODUCTION

Multichannel Analysis of surface waves (MASW)

is a recently developed non-destructive technique

to determine the sub-soil stratigraphy. The overall

procedure of a MASW survey comprises of Data

acquisition, Dispersion analysis and Inversion of

the extracted dispersion curve to obtain the

subsurface stratigraphy in terms of the shear wave

velocity profile [1].

This paper reports about the dispersion analysis

conducted based on the raw records obtained from

the experimental investigation carried out at IIT

Guwahati to determine the subsurface stratigraphy

at a particular test site. Active MASW survey was

conducted with the application of a sledgehammer

(weight of 10 kg) and a Propelled Energy

Generator (PEG of weight 40 kg) as the energy

sources to generate wave-fields across the soil

medium. 4.5Hz geophones were used as receivers

which provide the output in the form of voltages

generated due to the motion of the elements within

the receivers. The offset and receiver spacing were

maintained to be 2 m. Data acquisition involved

with the acquisition of multichannel field records

where the raw data was acquired at different

sampling frequency from the field based on

different geometrical configurations of the

arranged geophones. Based on the collected

records, dispersion analysis was conducted.

In this paper, various aspects of dispersion analysis

are discussed, with the emphasis on the extraction

of experimental dispersion curve. Two

commercially available software, EasyMASW and

SurfSeis have been utilized for the generation of

dispersion image, from where manual picking

technique has been used to extract the theoretical

dispersion curve. However, since this technique

becomes subjective, an automated dispersion point

picking has been developed with the aid of

indigenous Matlab coding which works on the

principle of identification of dispersion points

having the highest accumulation of energy during

wave propagation.

BASIC CONCEPT OF DISPERSION

Dispersion of waves is the phenomenon related to

the waves travelling through a wide area with

different frequency, wavelength and phase velocity

and with gradually enhanced wave-front as shown

in Fig. 1. Dispersion analysis is carried out to

obtain the dispersion curves which are the plots of

phase velocity versus frequency of the propagating

waves. It is obtained by Fast Fourier Transform

(FFT) of the time-space domain signal to achieve

the result in frequency domain.

When more than one phase velocities exist for

given frequency, it is referred as multimodal

dispersion. The slowest one in this case is called






fundamental mode and next faster one is called as

the first higher mode and so on as in Fig. 2.

Fig 1 Concept of Dispersion [1]

Fig 2 Dispersion curve of fundamental mode and

multi mode [1]

In contrast to the earlier used wave analysis

techniques as mentioned in [2], the multichannel

approach does not attempt to calculate individual

phase velocity, but constructs an image space

where dispersion trends are identified from the

pattern of energy accumulation in this space.

Thereby, necessary dispersion curves are extracted

by following the image trends of maximum energy

accumulation. All types of seismic waves

propagating are imaged if they take any significant

energy. In this imaging process, a multichannel

record in time-space (t-x) domain is transformed

into either frequency-wavenumber (f-k) or

frequency-phase velocity (f-C) domain. The

traditional f-k method is of the former type as in

[3], whereas the π-ω transformations and the

phase-shift method as in [2] are two instances of. It

is generally known that the f-k method results in the lowest resolution in imaging, whereas the

phase-shift method achieves higher resolution than

π-ω methods as in [4].

EXPERIMENTATION PROGRAM

Active MASW survey was carried out. Waves

were generated at the source by striking at a plate

by a sledgehammer of weight 9.82 Kg and the

geophones being placed in a linear array. In the

present case reported, 24 numbers of geophones

receivers were used. Figure 3 shows a schematic of

the active MASW survey [1] adopted for the

particular study. Photograph 1 depicts the various

components of the Active MASW survey used in

the present experimentation program. Field

experiment was carried out in the plain terrain near

the Civil Engineering Department, IIT Guwahati.

Sampling frequency was varied during the conduct

of the experiment and data acquisition. Samples

were obtained at the sampling frequencies of

15000Hz, 3750Hz, 500Hz, and 50Hz. For each

sampling frequency, four hammer impacts were

provided where each hammer impact is used to

acquire and the received signals was stored. The

parameters- offset distance and geophone spacing

were maintained constant to 2m.

Fig 3 Schematic of Active MASW survey [1]

In addition to the sledge-hammer (9.82 kg),

experiment was carried out by generating energy

out of a PEG (Propelled Energy Generator)

Hammer (40 kg) as shown in Photograph 2. The

specifications of the PEG used are as follows: (a)

Hammer weight: 36 kg (b) Hammer drop height:

36-43 cm, (c) Cycle time: 3-5 seconds, (d) Motor

engine size: 12V, (e) Electric, power supply

requirements: 12V Battery, (f) Impact frequency

band: 10-250 Hz, and (g) Size of impact plate: 46 x

46 x 2.5 cm.

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Photograph 1 Components of the MASW survey

POST-PROCESSING OF RAW FIELD DATA

Matlab Coding The analytical investigation for the dispersion

curve was carried out as per the phase-shift method

proposed by [2] to obtain the dispersion image. As

per [4], the phase-shift method achieves higher

resolution than π-ω and f-k methods. In this phase-

shift method [2], the summed up amplitude of

frequency domain records is determined with

varying phase velocity. Figure 4 depicts the

dispersion imaging scheme as described in [2].

Photograph 2 PEG-40 kg hammer

First, the amplitude of each channel was

determined and subsequently the phase-shift for

each channel was calculated, followed by the

development of a plot of summed up energy with

respect to the varying phase velocity. To obtain the

dispersion curves, all the 2-D curves at different

frequencies are assembled to obtain a 3-D image

showing the energy distribution as the function of

phase velocity and frequency. A Matlab code has

been developed as a preliminary attempt to obtain

the summed up amplitude and the dispersion curve.

First, for the given sampling frequency and time

period, an N-channel record mrn is defined as an

array of N traces. mrn is the time-domain record as

shown in Fig. 5 as obtained from the data

acquisition system (DAQ).


Fig 4 Dispersion imaging scheme [2]

Fig 5 Traces obtained from the DAQ

Fig 6 Time-Domain signal

Through the help of Matlab code, the time-domain

and frequency-domain curves were obtained.

Figure 5 depicts the time domain signals as

collected by the geophone array (Sampling

frequency Fs–15000 Hz). Figure 6 show the 3D

plot of time, distance and amplitude. The time-

domain representation (Fig. 5) identifies the travel

of the wave through the geophone array. These

time-domain signals are converted into frequency

domain by the Fast Fourier Transformations (FFT)

which is represented as MRn array in MATLAB

coding. MRn contain frequency domains signals

and it is represented by complex numbers. Fast

Fourier transformation is an algorithm in which the

frequency-domain and the time-domain are inter-

convertible. To use FFT for the purpose of

conversion to frequency domain, NFFT is defined

based on the determination of the next power of 2

from signal length for the purpose of padding.

Padding is to cut the excess signal from the next

power of 2 and to add to the deficient signals for

the purpose of easy matrix analysis. The padding is

done in order to maintain the length of the sample

as of form 2n (n power of 2 representing the

nearest length of the actually recorded sample).

Figure 15 shows the frequency content and its

single-sided spectral amplitude. Single-sided

amplitude is the absolute amplitude (considering

both the real and imaginary coefficients) of voltage

[in millivolts (mV)] in one-side. MRn obtained is

the product of amplitude and phase of the collected

signal. Amplitude changes with both offset and

angular frequency due to spherical divergence,

attenuation and the source spectrum characteristics.

Phase is determined by the phase velocity of each

wave of different frequency. To determine the

frequency of waves and for the distribution of

waves, the sampling frequency was divided by 2

(ie. Fs/2). This frequency is known as Nikos or

Nyquist frequency which restricts the mirroring on

the obtained curve. Nyquist frequency is always

half the sampling frequency, which, if not

maintained, the problem aliasing become

substantial. It has also been stated that construction

and proper analysis of a signal is possible when the

sampling frequency is at least twice the maximum

frequency sampled as mentioned in [5]. As shown

in Fig. 18, the frequency domain signals help to

identify the modes of vibration of the soil system.

EasyMASW

EasyMASW is commercial software available for

characterization of the subsurface in terms of shear

wave velocity profile up to a depth of 30 m (Vs-30)

(for active surveys). It aids to provide the idea

about soil type (based on an inbuilt approximate

database), the velocity profile and geotechnical

parameters such as shear modulus, elastic and

edometric modulus of the soil (based on the

specified category of the soil).

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SurfSeis

SurfSeis software was developed at the Kansas

Geological Survey to process seismic data using

the multichannel analysis of surface waves

(MASW) method.

RESULTS AND DISCUSSIONS

The data obtained was first processed by

EasyMASW software. The dispersion images were

obtained for different sampling frequencies.

Fig. 7a Dispersion curve for sampling frequency

500 Hz obtained from EasyMASW

Fig. 7b Dispersion curve for sampling frequency


Figure 7a depicts dispersion image with the

sampling frequency of 500Hz which was obtained

by EASYMasw Software. From this image space,

dispersion curve trend is identified and extracted to

carry out the inversion. Here, the first order and

higher order mode can be easily identified. Figure

7b-c shows the dispersion curve obtained with

sampling frequency of 3750 Hz and 15000 Hz,

respectively.

Fig. 7c Dispersion curve for sampling frequency


It has been observed that the records collected

using 50Hz sampling frequency is not at all

sufficient to produce a dispersion image with

clarity. Lower sampling frequencies have higher

sampling intervals, and hence, pick-up too less

number of bits of information from the wave

propagating through the geophone, which might

result in skipping of many peak and valley features

of the wave. In active survey, the waves produced

due to impact are mostly high frequency waves

penetrating smaller depths in the subsurface. For

these waves, sampling frequency of 50Hz proves to

be insufficient, and hence, results in an obscure

dispersion image, which fails to provide any

information. Hence, it is suggested to go for higher

sampling frequency in order to obtain the best

possible dispersion curve. Figure 7d shows the


dispersion image and curve obtained using a

sampling frequency of 50Hz, The images being too

blurred and fails to provide any further information

regarding the subsurface stratigraphy.

Fig. 7d Dispersion curve for sampling frequency


Fig. 8a Dispersion curve for sampling frequency

15000 Hz obtained from SurfSeis

Fig. 8b Dispersion curve for sampling frequency


Fig. 8c Dispersion curve for sampling frequency


The effect of sampling frequency on the dispersion

image was studied using Surfseis software. Figure

8a-c shows the image obtained from the Surfseis

software. It was observed that the 50Hz sampling

frequency cannot be processed at all to extract any

information out of it.

Along with the data obtained by sledgehammer,

PEG data was also analysed using Surfseis and

EasyMasw. Figure 9a-b shows the dispersion

image obtained using Propelled Energy Generator

(PEG) and it was observed that there is heavy

accumulation of energy in the fundamental mode at

around frequency of 0-10Hz.The First order and

higher order mode can be easily identified at higher

frequencies.

A Matlab Program was developed to obtain the

dispersion image and thereby to identify the

dispersion curve and extracting the dispersion

curve with highest accumulated energy. Attempt

was made to automatically pick up the highest

accumulated energy. First, the time cut procedure

was carried out which will process the traces with

considerable energy content and part of those

traces where energy attenuate was cut out. After

time cutting, spikes in the time-domain signal

obtained as shown in Fig. 6 were removed. Spikes

are the unwanted transient which may arise due to

vibrations in the subsurface by heavy machine or

heavy vehicle which is needed to eliminate or it

may cause erroneous results. Figure 10 is the time-

domain signal after removal of spikes and time cut.

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Fig 9a Dispersion curve obtained from Surfseis for

3750Hz sampling frequency using PEG

Fig 9b. Dispersion curve obtained using PEG for

3750Hz sampling frequency from EasyMASW

Fig 10 Time-domain signal after removal of spikes

After the removal of spikes the time domain signal

was squared up to eliminate the negative effect

(Fig. 11). The energy was accumulated on the

upper side of the axis. Then the squared up

amplitude was normalized to understand the

relation of energy all the channels or to compare

energies of each channel. Figure 12 shows the

Normalized squared time domain curve for 24

channels.

Fig 11 Squared-up time domain signal

Fig 12 Normalized squared-up time domain signal

Fig 13 Cumulative normalized squared-up

amplitude


The cumulative energy is found out which has been

shown in Fig. 13. Figure 14 shows the processed

time-domain signal of geophones which is ready

for FFT. Figure 15 is the Single sided Fourier

amplitude obtained by Fast Fourier transform

(FFT). The single sided amplitude spectra was then

normalised and squared up to obtained the

processed frequency records as shown in Fig

ures16-18 respectively.

Fig 14 Processed time-domain signal after time-cut

Figure 19 show the dispersion Image found by

Matlab code. Here, the highest accumulated energy

has to be identified and extracted to be used for the

inversion. Higher modes can also be identified

from this image.

Fig 15 Single sided Fourier amplitude

The processed frequency domain record was used

to obtain dispersion image space. Different type of

curve trend was identified. The fundamental mode

and higher order mode was identified according to

their energy accumulation pattern. The highest

accumulated energy was picked up using the

suitable Matlab subroutines which can be further

extracted and can be used for inversion. However,

more detailed study is required in this regard as the

resolution of image space obtained is quite low.

Figure 20 shows dispersion points selected by the

automated picking scheme developed in Matlab

which can be subsequently used for the inversion

procedure.

Fig 16 Normalized FFT records

Fig 17 Cumulative squared up normalized FFT

records

Fig 18 Processed frequency domain record

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Fig 19 Dispersion Image for 15000Hz sampling

frequency as obtained through Matlab coding

Fig 20 Picking up of highest accumulated curve

CONCLUSIONS

Active MASW survey was carried out in plain

terrain of IIT Guwahati in between M and N blocks

of the academic complex. Data were obtained at

different sampling frequency of 15000Hz, 3750Hz,

500Hz and 50Hz. After analyzing the data, it was

found out that dispersion curves obtained with the

EasyMASW and SurfSeis software closely

resembles each other for the different sampling

frequency of 15000Hz, 3750Hz and 500Hz.

Dispersion image space for 50Hz sampling

frequency could not be obtained. Additionally,

some data was collected using PEG and was

analyzed with EasyMASW and SurfSeis software.

Analysis result shows that there is a heavy

accumulation of energy in the fundamental modes.

EasyMASW and SurfSeis extracts the dispersion

curve based on the user discretion which may lead

to missing the energy accumulation peaks and end

up with the user selecting a point which should

have been actually absent in the dispersion curve.

Therefore, attempt has been made to generate

automated curve extraction program using Matlab

from the image space which thus overcomes the

problem faced in the manual picking of the

dispersion points.

REFERENCES:

1. http://www.masw.com/index.html

(Last referred: 28/08/2015)

2. Park, C.B., Miller, R.D., and Xia. J., (1998),

Ground roll as a tool to image near surface

anomaly, 68th Annual International Meeting of

SEG, Expanded Abstracts. 874-877.

3. McMechan and Yedlin,(1981). Analysis of

dispersive waves by wave field transformation.

Geophysics. 869-874.

4. Dal Moro,G., Pipan, M., Forte. E., Finneti, I.

(2003).Determination of Rayleigh waves

dispersion curves for the near surface

applications in unconsolidated sediments,

Expanded Abstract. Society of Exploration of

Geophysicist, 1247-1250

5. http://en.wikipedia.org/wiki/Nyquist_frequency

(Last referred: 28/08/2015)

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