monitoring and numerical modelling of induced and ... · 31.05.2018 · monitoring and numerical...
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Monitoring and Numerical Modelling of Induced and
Triggered Seismicity in a Deep Sublevel-Stoping Mine Francesca De Santis (1,2), Yann Gunzburger (2), Vincent Renaud (1)
Isabelle Contrucci(1) , Pascal Bernard(3)
(1) Institut National de l’Environnement Industriel et des Risques (Ineris); (2) GeoRessources, Université de Lorraine; (3) IPGP
Objective of the study
2
Advantages Limits
Global in site
measurements
Precision of detection
and location
Real time monitoring No detection of
aseismic deformation
Insight into fractures
dynamic Local measures
Advantages Limits
Long-term prediction Needs lots of geo-
mechanical information
Simulation of different
excavation sequences
High computational
requirements
Global view of stress
changes
Why compare seismic and model data? Microseismic Monitoring
Real-time monitoring for short-term prevention
Numerical modelling Optimization of future excavations
Better understanding of interactions between stress changes and seismic activity generation
Gaining additional insights into failure processes
Improving long-term and short-term prevention
Finding possible links
Geographic and Historical context
Garpenberg mine
Sweden’s oldest mining area still in operation
Deposits first mined in in the 13th century
Acquired by Boliden in 1957 (300.000 tonnes/year)
Discovery of Lappberget orebody in 1998
Production moved in Garpenberg North in 2004 1200 Z
200 Z
800 Z
600 Z
400 Z
0 Z
1000 Z
North South
Schematic vertical profile of Garpenberg mine
Mined out areas
3
South
North
South
North
SWEDEN
zinc
silver
copper lead
gold
Extracted ore in 2016 2 622 000 t
Ineris monitoring network
Lappberget monitoring network (De Santis et al., 2017; Tonnellier et al., 2016)
3C
1C
Z [m
]
Lappberget
~ 3 km
Dammsjön
Kyrkan-Tyskgården
Gransjön
Kaspersbo
Blo
ck 1
25
0 L
app
ber
get
Production & Injuries
Amount of produced ore [kt] (1957-2014) Number of injuries (1996-2014)
0
500
1 000
1 500
2 000
2 500
1957 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 0
2
4
6
8
10
12
14
16
18
1996 1999 2002 2005 2008 2011 2014
Garpenberg mine
4
Improve automation and digitalization Mass mining methods
Microseismic monitoring
Mining method and sequence
5
Lappberget orebody
Sublevel stoping method with backfilling
Mining sequence
Z
Y
X
3D view between 2 levels Y
X
Primary drift
Secondary drift
Orebody profile
Plan view
Drift excavation: Development Blasts X
1107
1132
1157
1182
1207
1232
1257
13 12 11 10 9 8 7 6 5 22 21 20 19 18 17 16 15 14 23
2014
2015 - 2016
25
m
Stope excavation: Production Blasts Vertical section Z
November 2015 December 2016 Stope 13 Stope 13
Lappberget orebody
Lack of data:
Characteristics of fractures, joints or faults
Mechanical properties of rock types
No 3D information except for the orebody
Local geology
X
Z Y
Orebody
6
Lithology Dolomite
Limestone
Orebody
X
Y
X
Y
Very weak zones
Weak zones
Mechanical properties
Materials Young
modulus E [MPa]
Poisson’s ratio
n
Density r
[kg/m3]
Ore 66000 0.2
3030 Limestone 57000 0.18
Weak 20000 0.3
Very weak 2000 0.4
Weak rock masses
7
Analysis of Lappberget Microseismic Activity
between 2015 and 2016
Mining Production & Microseismic Activity
8
1000-3000m3 3000-4000 m3 4000-5000 m3 5000-6000m3 ≥ 6000 m3
Extracted rock mass volume:
Cu
mu
lati
ve M
SE n
um
ber
MSE
nu
mb
er
Time [year-month]
1 3 2 4 5
Microseismic swarms Mined Stopes
1
2
3 4 5
Stope 13
Space-time distribution
Mining Production & Microseismic Activity Cluster of MSE
9
Z [m
]
Central Cluster
Right Cluster
Mic
rose
ism
ic a
ctiv
ity
Min
e p
rod
uct
ion
1107
1132
1157
1182
1207
1232
1257
13 12 11 10 9 8 7 6 5 22 21 20 19 18 17 16 15 14 23
Central Cluster Right Cluster
Exploited Stopes 2014 - 2016
Period 2015 - 2016
X [m]
1e3
Central Cluster Right Cluster
MSE number & stopes volumes on a sliding window along X direction
Good correlation
Bad correlation
Central Cluster
Right Cluster
Bad correlation
Mining Production & Microseismic Activity Cluster of MSE
Z [m
]
Central Cluster
Right Cluster
Second period
Z [m
]
Right Cluster
Central Cluster
First period
First period Second period
1 2 3 4 5
Time [year-month]
Cu
m. M
SE n
um
ber
Cum. CC events
Cum. RC events
10
1
2
3 4 5
Stope 13
Seismic source characteristics Fracture size and Apparent Stress
11
Source radius ⇒ from 0.3 m up to 32 m
Median RC radius = 0.9 m
Median CC radius = 3 m
Fracture dimension Moment magnitude
Seismic moment [J]
Seis
mic
en
ergy
[J]
(Wyss and Brune, 1968)
Apparent stress ⇒ measure of stress release at a seismic source
Bigger stresses in the Right Cluster area
Seismic source characteristics Apparent stress
12
Level 1157 Level 1157
Contour maps of apparent stress
February 2015 - May 2016 June 2016 - December 2016
log10(meanAppStress) log10(meanAppStress)
First period Second period
Conclusions on seismic data analysis
13
Central Cluster Right Cluster
In agreement with production
in the area
Linked with remote blasting
in stope 13
Active since the beginning of
the monitoring
Activated at final stages of
stope 13 exploitation
+
Influenced by local geology
Bigger fractures Smaller fractures
Lower apparent stress values High apparent stress values
14
Numerical Modelling
Simulation of the exact mine sequence between 2015 and 2016
15
Numerical Model Geometry & Meshing
1. Galleries & Stopes
2. Weak & Very weak zones
3. Orebody
16
Numerical Model
Z Z = - 578
Z = - 1088
Upper levels mesh = 5 m
Z Z = 0
Z = - 1104
Z = - 1260
Z = - 1500
Upper levels Ore extension
Limits of the model
Box mesh = 20 m
Geometry & Meshing
Z = 0
Z = - 1500
Numerical Model Boundary Conditions & Mechanical Parameters
Initials & Boundary Conditions
Elastic parameters
Hoek-Brown parameters
Materials Young
modulus E [MPa]
Poisson’s ratio
n
m s sC
[MPa]
Density r
[kg/m3]
Ore 66000 0.2 10 0.112 188
3030 Limestone 57000 0.18 10 0.112 110
Weak 20000 0.3 1 0.001 30
Very weak 2000 0.4 0.63 0.00024 10
Paste 500 0.2 - - - 2000
17
Hoek-Brown failure criteria
⇒
Brittle failure criteria parameters
Materials mr s3b-d sr ξr bm b1
Ore 2 sC* (s)1/2 s*1e-5 0.0025 tan(15°) 750
Failure criteria
Brittle failure criteria
0
50
100
150
200
250
300
350
400
-0,002 0 0,002 0,004 0,006
Deviatoric stress
s1 - s3 (MPa)
Axial strain (-)Lateral strain (-)
s3 = 0.01 MPa
s3 = 1 MPa
s3 = 5 MPa
s3 = 10 MPa
s3 = 25 MPa
s3 = 50 MPa
s3 = 75 MPa
s3 = 60 MPa
(Souley et al., 2018)
18
Numerical Model Model Results
Deviatoric stress* [MPa]
X
Y
X
Y
X
Y
Step 15 – 2015/03/15
X
Y
Step 41 – 2016/08/03
Level 1182
Level 1182 Level 1182
Level 1182 Step 25 – 2016/02/09
Step 52 – 2016/12/28
*
Stope 13 Stope 13
Stope 13 Stope 13
19
Step 15 – 2015/03/15
Step 41 – 2016/08/03
Level 1182
Level 1182 Level 1182
Level 1182 Step 25 – 2016/02/09
Step 52 – 2016/12/28
X
Z
X
Z
X
Z
X
Z
Numerical Model Model Results
Deviatoric stress* [MPa]
20
Seismic Data and Model Data comparison
21
Seismic Data & Model Data 1st approach of comparison: individual measures
Seismic parameters Punctual measures at seismic
events locations
Model parameters Measures within the whole space
⇔
Model parameters calculated inside meshes at the position of MSE location
Seismic parameters
E – seismic energy
M0 – seismic moment
MW – moment magnitude
fc – corner frequency
sapp – apparent stress
Vapp – apparent volume
Δσ – stress drop
Model parameters
Stress variables Strain variables
s1 – max. principal stress
e1 – max. principal strain
s2 – inter. principal stress
e2 – inter. principal strain
s3 – min. principal stress
e3 – min. principal strain
sij – stress tensor eij – strain tensor
sq – Von Mises stress
eq – Von Mises strain
22
Seismic Data & Model Data Qualitative Analysis
Model parameter Seismic parameter
Mean deviatoric stress Mean apparent stress
sq [MPa] log10(meanAppStress)
Stope 13 Stope 13
23
Seismic Data & Model Data Quantitative Analysis: Principal Component Analysis
q
r = 0.0296
r = 0.0287
r = 0.0512
r = -0.0196
r = 0.0441 fc
E
M0
E
M0
E
M0
fc
E
M0
fc
q2 1/q
sqrt(q) fc
24
Seismic Data & Model Data 2nd approach of comparison: cumulated measures
Cumulated model parameters calculated inside the spheres of 10 m radius around each seismic events location
Seismic parameters
E – seismic energy
M0 – seismic moment
Va – max. principal stress
As – Source area
Model parameters
Elastic Plastic
Volumetric elastic energy
Volumetric plastic energy
Shear elastic energy
Shear plastic energy
Total elastic energy
Total plastic energy
Plastic volume
Mean Von Mises stress and strain
Mean of max. shear strain
Volumetric strain
Shear strain
Seismic parameters Punctual measures at seismic
events locations
Model parameters Measures within the whole space
⇔
25
Seismic Data & Model Data
Apparent volume ⇒
Plastic volume ⇒ volume of plastic zones in the spheres
Time [year-month]
Cu
mu
lati
ve p
last
ic v
olu
me
[m3]
Cu
mu
lati
ve a
pp
aren
t vo
lum
e [m
3]
1e4 1e6
Seismic parameter
Model parameter
CC-Va
CC-Vp
RC-Va RC-Vp
CC ⇒ Central Cluster RC ⇒ Right Cluster
Va
Vp
Time [year-month]
1e4
Cu
mu
lati
ve p
last
ic v
olu
me
[m3]
Cu
mu
lati
ve a
pp
aren
t vo
lum
e [m
3]
Seismic parameter
Model parameter
Qualitative Analysis
26
Seismic Data & Model Data
Source area ⇒
Volumetric strain ⇒
Time [year-month]
Time [year-month]
Cu
mu
lati
ve a
pp
aren
t vo
lum
e [m
3]
Cu
mu
lati
ve V
on
Mis
es s
trai
n [
mm
/m]
1e4
Cu
mu
lati
ve s
ou
rce
area
[m
2]
Cu
mu
lati
ve v
olu
met
ric
stra
in [
mm
/m]
As
εV
Seismic parameter
Model parameter
Seismic parameter
Model parameter
Apparent volume ⇒
Von Mises strain ⇒ Va
εq
Qualitative Analysis
)(t r e
27
Discussion and Perspectives
Seismic & numerical model shows increasing stresses around the main production area
Spatialization problem • Uncertainties in events location & model uncertainties • Reactivation of preexisting structure not taken into account in the model
Cumulating seismic and numerical parameters in space correlations are improved
Improvement of events detection and location • Detection: bigger number of seismometers and continuous recordings • Location: new location methods based on double difference and multiples analysis
Include fractures in the model • Bigger fractures of CC may be modeled based on focal mechanism of MSE • Smaller fractures of RC cannot be included in the model with the actual mesh ⇒ reduce mechanical properties of some meshes?
Taking creep into account • Seismic swarms are long lasting in time (more than 1 month) • Stress measurements have sown a differential response of deformations during time
Questions ? References
DeSantis, F., Contrucci, I., Lizeur, A., Tonnellier, A., Matrullo, E., Bernard, P., Nyström, A., 2017. Numerical approach for evaluating microseismic array performances: case study of a deep metal mine monioring network, in: Proceedings of the 9th International Symposium on Rockbursts and Seismicity in Mines.
Tonnellier, A., Bouffier, C., Renaud, V., Bigarré, P., Mozaffari, S., Nyström, A., Fjellström, P., 2016. Integrating microseismic and 3D stress monitoring with numerical modeling to improve ground hazard assessment, in: Proceedings of the Eighth International Symposium on Ground Support in Mining and Underground Constructions. eds. E. Nordlund et al., University of Technology, Luleå, Sweden.
Souley, M., Renaud, V., Al Heib, M., Bouffier, C., Lahaie, F., Nyström, A., 2018. Numerical investigation of the development of the excavation damaged zone around a deep polymetallic ore mine. International Journal of Rock Mechanics and Mining Sciences, 106, 165-175.
Wyss, M., Brune, J.N., 1968. Seismic moment, stress, and source dimensions for earthquakes in the California-Nevada region. J. Geophys. Res. 73, 4681–4694.
28
1000-3000m3 3000-4000 m3 4000-5000 m3 5000-6000m3 ≥ 6000 m3
Extracted rock mass volume for Production Blasts:
29
n° events per hour
cum. events number
Time [year-month]
Cu
mu
lati
ve M
SE n
um
ber
MSE
nu
mb
er
Mining Production & Microseismic Activity Space-time distribution
X ≈ 300 m3
Extracted rock mass volume for Development Blasts:
Pro
du
ctio
n B
last
s
Dev
elo
pm
en
t B
last
s