Post on 29-Mar-2021
Seismic Hazard and Risk Assessment for Induced SeismicityEllen M. Rathje, PhD, PE, F.ASCE
Janet S. Cockrell Centennial Chair in EngineeringDepartment of Civil, Architectural, and Environmental Engineering, University of Texas, Austin
Prof. Patricia Clayton, Prof. Brady CoxIason Grigoratos, Farid Khosravikia, Meibai Li, Michael Yust
Department of Civil, Architectural, and Environmental Engineering, University of Texas, AustinDr. Alexandros Savvaidis
Bureau of Economic Geology, University of Texas, Austin
Seismic Risk Assessment
Risk = Hazard
x Exposure
x Vulnerability
x Consequences
Measure of ground shaking and its probability
Characterization of built environment and inhabitants
Susceptibility of the exposure to damage/undesirable consequences
$$, number of people adversely affected
Beer!Food!
Clayton, Khosravikia, Rathje, Grigoratos
Rathje, Grigoratos, Cox, Li, Yust, Savvaidis
Seismic Hazard AssessmentRisk = Hazard x Exposure x Fragility x Consequences
Seismic Source Characterization
Ground Motion Characterization
(Reiter, 1990)
Requires:• Rate of earthquakes• Magnitude (M)
distribution• Locations
Requires:• Ground shaking as a function of
M, distance (R), and soil/rock conditions (Vs30)
• Variability
For tectonic EQs: • Stationary• Use historical EQ
catalog
For induced EQs:• Time-dependent• Relate seismicity to oil/gas
operations (e.g., injection)
For induced EQs:Use recordings from events in
geologically similar regions
4
Temporal and Spatial Variations in Seismicity: Oklahoma
Seismicity
Wastewater Injection
Temporal Spatial
5
Time-Dependent Gutenberg-Richter Relationship
𝜆𝜆 = 10𝑎𝑎𝑡𝑡𝑡𝑡𝑡𝑡−𝑏𝑏�𝑚𝑚
𝜆𝜆[𝑡𝑡] = 10𝑎𝑎𝑡𝑡𝑡𝑡𝑡𝑡 + 𝑣𝑣𝑙𝑙𝑙𝑙𝑙𝑙[t] � 10𝚺𝚺 � 10−𝑏𝑏�𝑚𝑚
monthly lagged distributed
injection rate
Seismogenic Index
with 𝑡𝑡𝑙𝑙𝑎𝑎𝑙𝑙[t] = 𝜽𝜽𝑣𝑣[𝑡𝑡]
monthly distributed injection rate
Free parameters:𝜽𝜽 and Σ from monthly injection and seismicity data𝑙𝑙𝑡𝑡𝑡𝑡𝑡𝑡 and b from background seismicity (<2009)
Spatial and temporal resolution:Monthly injection/seismicity for 5-km x 5-km blocks
Semi-empirical model with parameters derived from seismicity and injection data
6
Application to OklahomaMonthly EQs and injection volumes 2000-2018
Cumulative injection volume (m3) 2006-2018 Spatially Varying Model Parameters
Gridded and Spatially Distributed Injection VolumesCumulative 2006-2018
Seismogenic Index (Σ) Hydromechanical Parameter (θ)
7
Simulated seismicity rates
Sub-Regions
Full Study Area
Calibration: Jan 2006 – Dec 2017
ObservedSimulated
8
Hindcasting: calibrate parameters through Dec 2014
Calibration thru Dec 2014
Calibration Forecast
Model performance when we calibrate parameters thru Dec 2014 and then forecast the EQs, given the injection ratesCalibration thru
Dec 2017
Observed
Simulated
9
Hindcasting: calibrate parameters through **Dec 2011**
Calibration thru Dec 2011
We need to spatially extrapolate Σ and θ
Calibration Forecast
Model performance when we calibrate parameters thru Dec 2011 and then forecast the EQs, given the injection ratesCalibration thru
Dec 2014
Ground Motion CharacterizationEmpirical Ground Motion
Model (GMM)
Peak
Gro
und
Acce
l, PG
A (g
)
Distance (km)
Rock Conditions
Characterization of Shear Wave Velocity (Vs30)
Zalachoris and Rathje (2019) EQ Spectra
10
Using data from Texas, Oklahoma, and Kansas
Ground Motion Model (GMM) Development• Events in TX, OK and KS with M > 3.0 between 2005-2017• Recordings from TX, OK and KS seismic stations (past and existing)
2.5
3
3.5
4
4.5
5
5.5
6
1 10 100
Mom
ent M
agni
tude
, MW
Hypocentral Distance, Rhyp (km)
(a)• 4,815 records• 398 Events• 223 Stations
4.5 < M < 5.0 M > 5.0
M < 3.5 3.5 < M < 4.0 4.0 < M < 4.5
Gulf Coast Plain Stations
Events Stations
Zalachoris and Rathje (2019) EQ Spectra
Assessment of Existing GMMs
VS30 = 760 m/s VS30 = 760 m/s
Hassani & Atkinson: NGA-East (2015)
Atkinson (2015): PotentiallyInduced EQs (PIEs)
• Develop empirical adjustment for Hassani and Atkinson (2015) GMM using TX-OK-KS ground motion data
Reference Empirical Approach
Underprediction
Overprediction
12
Adjustment Factors
𝐶𝐶 = 𝑡𝑡𝑐𝑐𝑐𝑐𝑐𝑐𝑡𝑡.
ln𝐹𝐹 = 𝐶𝐶 + 𝐹𝐹𝑅𝑅 + 𝐹𝐹𝑆𝑆 + 𝐹𝐹𝑀𝑀 ⟹
Overall Adjustment:
At each frequency, f:
Distance Scaling:
VS30 Scaling:
Magnitude Scaling:
Final Model for Adjustment Factors:
𝐹𝐹𝑅𝑅 = �0 𝑅𝑅 ≥ 𝑅𝑅𝑏𝑏
𝑙𝑙 ∙ ln �𝑅𝑅𝑅𝑅𝑏𝑏� 𝑅𝑅 < 𝑅𝑅𝑏𝑏
𝐹𝐹𝑆𝑆 = �0 𝑉𝑉𝑆𝑆30 > 𝑉𝑉𝐶𝐶∗
𝑡𝑡∗ ∙ ln�𝑉𝑉𝑆𝑆30
𝑉𝑉𝐶𝐶∗� 𝑉𝑉𝑆𝑆30 ≤ 𝑉𝑉𝐶𝐶∗
𝐹𝐹𝑀𝑀 = 𝑏𝑏0 + 𝑏𝑏1 ∙ 𝑀𝑀
𝑅𝑅 = �𝑅𝑅ℎ𝑦𝑦𝑦𝑦2 + ℎ𝑡𝑡𝑒𝑒𝑒𝑒2 heff: effective depth (Yenier & Atkinson, 2014)
⟹ 𝑃𝑃𝑆𝑆𝑃𝑃𝑇𝑇𝑇𝑇−𝑂𝑂𝑂𝑂−𝐾𝐾𝑐𝑐 = 𝐹𝐹 ∙ 𝑃𝑃𝑆𝑆𝑃𝑃𝐻𝐻𝑃𝑃15
Zalachoris and Rathje (2019) Ground Motion ModelM ≥ 5.0, Vs30 = 760 m/s
0
1
2
3
4
5
6
7
8
100 1000
Am
plifi
catio
n (V
S30
= 76
0 m
/s)
VS30 (m/s)
T = 1.0 sec BSSA14
Adjusted BSS14
NGA-East
VC
Adjusted Vs30 Scaling
ZR19NGA-West2
NGA-East
Shear Wave Velocity CharacterizationP-wave Seismogram Method
Incident PReflected SV
Reflected P
UZ
URx
ii
j
Ni et al. (2014), Kim et al. (2016)
VSZ = 1578 m/sVS30 = 1107 m/s
VSZ = 519 m/sVS30 = 436 m/s
Vs30 = 1107 m/s
Vs30 = 436 m/s
+±
=
Z
R
Z
R
S
UUp
UUjj
V
2
2coscos2
2
15
Shear Wave Velocity CharacterizationP-wave Seismogram Method
Incident PReflected SV
Reflected P
UZ
URx
ii
j
Ni et al. (2014), Kim et al. (2016)
+±
=
Z
R
Z
R
S
UUp
UUjj
V
2
2coscos2
2
Vs30 at Recording Stations
16
In Situ Vs30 Measurements
Field Measurements
Performed by Cox and YustZalachoris et al. (2017) EQ Spectra
Development of Comprehensive Vs30 MapGeologic Proxy for Vs30:
Age and Rock Type
17
Geologic age Rock Type Group
Npts
Quaternary-Holocene (Outside of Gulf Coast) A/C 11 484Quaternary-Pleistocene (Outside of Gulf Coast) A/B/C/D 30 526Quaternary-Undivided (Outside of Gulf Coast) A/B/C 18 588
Quaternary-Holocene (In Gulf Coast) A/C 62 211Quaternary-Pleistocene (In Gulf Coast) B/C 7 242Quaternary-Undivided (In Gulf Coast) A/B 4 213
B 11 386C/D 30 466
E 1 696F 2 838
B/C/D 42 517E 37 765D 80 747E 12 971F 3 1638
Precambrian F 2 1434
Tertiary
Mesozoic
Paleozoic
𝝁𝒍𝒏𝑽 (𝒎/𝒔)
Inputs:1. Geologic Atlas of
Texas from BEG2. P-wave seismogram
estimated Vs303. In situ measurement of
Vs30
Intermediate Result: 1. Vs30 map based on
Geologic proxy
Final Output: 1. Kriged Vs30 map
Calculate residuals
Geostatistical interpolation
Vs30 observations
Mapping Approach
Geologic Age Rock Type
Group A: Alluvial and terrace depositsGroup B: Clay, silt, and loess; not alluviumGroup C: Sand and gravel; not alluvium.Group D: Mud/clay/silt/sand stone, conglomerate, marl, and shale Group E: Limestone and chalkGroup F: Chert, basalt, granite, and rhyolite
Rock Type Groups
Zalachoris et al. (2017) EQ SpectraLi et al. (in prep) EQ Spectra
#pts
Vs(m/s)
Comparison of Vs30 Maps
USGS Global Vs30 from Topographic Proxy
18
Texas-specific Vs30 Map(This study)
Ratio = Texas/USGS
Seismic Risk Assessment
Risk = Hazard
x Exposure
x Fragility
x Consequences
Measure of ground shaking and its probability
Characterization of built environment and inhabitants
Susceptibility of the exposure to damage/undesirable consequences
$$, number of people adversely affected
Beer!Food!
19
Fragility Curves
Ground Shaking Intensity (e.g. Peak Ground Acceleration,
Peak Ground Velocity)
Prob
abili
ty o
f Dam
age
Texas Building
California Building
0.5
1.0
Less fragile
More fragile
Used to predict the likelihood of
damage for a given ground shaking
intensity
20
Clayton and Khosravikia
21
Masonry Facades: Effect of Construction Practices
M5.7 Prague, OK (Nov. 2011)
In seismically-active areas:• Brick facades and chimneys are avoided• When used, more bracing is used to
prevent collapse
Unreinforced brick:• Commonly used in Texas• Known to be vulnerable in earthquakes
Fragility curves must reflect local construction practices
22
Effect of Ground Motions
Fragility curves must reflect ground motion characteristics
0 0.5 1 1.5 2 2.5 3
Tn [s]
0
1
2
3
4
5
6
Sa
/PG
A
Mean
“West coast” design motions
0 0.5 1 1.5 2 2.5 3
Tn
[s]0
1
2
3
4
5
6
Sa
/PG
A [
g]
Mean
Texas/Oklahoma motions
Earthquake Frequency ContentHigh frequency content = More
damage to buildings with low natural
period
Low energy for long period waves = Less damage to buildings with higher natural
period
23
Masonry Façade Fragility: Comparison of intensity measures
0 0.5 1 1.5 20
0.2
0.4
0.6
0.8
1
Sa05
Prob
abili
ty o
f dam
age
Rand 4Max MwMax PGAMin Dist
PSA(0.5) (g)
Prob
abili
ty o
f dam
age
PGA (g)
Prob
abili
ty o
f dam
age
PGV (cm/s)
Prob
abili
ty o
f dam
age
Sa(0.2) (g) PSA(0.2) (g)
Prob
abili
ty o
f dam
age
PGA PGV
PSA(0.2) PSA(0.5)
0 1 2 3 4 50
0.2
0.4
0.6
0.8
1
Sa(0.2) (g)
Prob
abili
ty o
f dam
age
22ecc-DS122ecc-DS228min-DS128min-DS2
Collapse
Cracking
Fragility of Different Types of Infrastructure
Residential Masonry Facades(TexNet - CISR)
Bridges (TxDOT)
(Clayton, Kurkowski, Khosravikia) (Clayton, Cox, Rathje, Williamson, Khosravikia, Potter, Prakhov, Zalachoris)
2016 M5.8 Pawnee, OK (source: P. Clayton) 2016 M5.8 Pawnee, OK
(source: P. Clayton)
Characterize Bridge Inventory
1920 1940 1960 1980 2000 20200
200
400
600
800
Year of Construction#
Brid
ges
Pre-stressed Concrete Girders(1970s-present)
Steel Girders(1950s-60s)
~53,000 bridges & culverts in Texas
26
Characterize Bridge Vulnerability
• Used computer models to simulate:• Different geometries (height, span length, etc.)• Different construction materials & designs• Wide range of ground motions
Verti
cal
Rigid linkBearing springAbutment/ foundation springs
Superstructure mass
Pounding Element
Bearing
AbutmentL L
L
T
T
Longitudinal
Tran
sver
se
Bearing
L
T
Tran
sver
se
Longitudinal
Developed in OpenSees Software
Bridge Fragility Curves in Texas
27
Slight Moderate Extensive Complete
Prob
abili
ty o
f dam
age
PGA (g) PGA (g) PGA (g) PGA (g)
0 0.2 0.4 0.6 0.8 1
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
0
0.2
0.4
0.6
0.8
1
MCSTEEL MSPC MCRC-Slab MSRC MSSTEEL SSPC MSRC-Slab
Steel and Pre-stressed
Concrete
Reinforced Concrete
Fragility Comparison
28
21.6Continuous steel girder bridges
Slight Moderate Extensive Complete
Prob
abili
ty o
f dam
age
PGA (g) PGA (g) PGA (g) PGA (g)
0 0.4 0.8 1.2 1.6 2
0
0.2
0.4
0.6
0.8
1
0 0.4 0.8 1.2 1.6 2
0
0.2
0.4
0.6
0.8
1
0 0.4 0.8 1.2 1.6 2
0
0.2
0.4
0.6
0.8
1
0 0.4 0.8 1.2 1.6 2
0
0.2
0.4
0.6
0.8
1
Texas HAZUS
Risk Assessment Framework
29
Potential earthquake Shaking
estimation
Damage estimation
Loss estimation
Monte Carlo Simulation
P(Damage State)1.0
0.0
SlightModerateExtensiveComplete
IMMonetary loss
Prob
abili
ty
Structural fragility models
Repair cost analysis
Ground motion model
Shaking level
Damage Estimation
30
Earthquake with M5
P(Damage State)1.0
0.0
SlightModerateExtensiveComplete
IM
A
PGA = 0.5 gFrom ground
motion models
Damage ProbabilitySlight 0.4
Moderate 0.2Extensive 0.1Complete 0.1
No damage 0.2
Slight damage
Hypothetical Earthquake with M5 in Dallas, Texas
One RealizationDamage Repair Cost
Slight 0.03 x Construction Cost
Moderate 0.08 x Construction Cost
Extensive 0.25 x Construction Cost
Complete 1.00 x Construction Cost
Damage Estimation
31
Hypothetical Earthquake with M5 in Dallas, TexasPGV (cm/s)
Loss Estimation
32
Earthquake with M5
Mean = 4.2 M Std. = 0.8 M
1.0 3.0 5.0 7.0 9.0
0.1
0.2
0.3
Scenario-based Loss Estimation
33
$ 4.2 M
Earthquake with M4
Earthquake with M5
Earthquake with M5.8
Mean of Monetary Loss
$ 16.2 M$ 0.04 M
Scenario-based Loss Estimation
34
$ 4.2 M
Earthquake with M4
Earthquake with M5
Earthquake with M5.8
Mean of Monetary Loss
$ 16.2 M$ 0.04 M
$ 0.09 M $ 31.3 M $ 284.3 M
Using HAZUS Fragility Curves
Scenario-based Regional Loss Estimates
35
21.6
Overestimation when region-specific info is NOT used
M4
M5
M5.8
3 times
8 times
17 times
36
Rapid Post-Event Consequence Assessment
• TxDOT implementation of ShakeCast software
• Input TxDOT bridge inventory & vulnerability (from research)
• Automatically retrieves ShakeMap from USGS minutes after event
• Integrate new GMM and Vs30 map
• Real-time report of inspection priorities
• Sends notifications to personnel
(ongoing project funded by TxDOT)
Probabilistic, Time-Dependent Risk Assessment: Oklahoma
Ground Motion Hazard
37
• Annual seismicity rates from Grigoratos et al. BSSA model calibrated through 2017• After 2017, injection rates assumed constant • Ground shaking from Zalachoris and Rathje (2019, EQS) GMM • Event-based annual PSHA from 10,000 simulations using OpenQuake (GEM Foundation)• Building inventory from 2010 census and 2018 replacement costs• Fragility curves for “low-code” buildings in the US (from GEM/USGS)
2015
Simulated Seismicity Monetary Loss Curves10% Annual Probability of Exceedance
38
Conclusions
• Seismic hazard and risk approaches for tectonic earthquakes can be adapted for induced earthquakes
• Key improvements required:‒ Semi-empirical models to forecast spatial and temporal
variations in seismicity‒ Ground motion models for induced earthquakes in the region of
interest‒ Detailed Vs30 maps using regional/local data‒ Fragility models to predict infrastructure damage for the
expected ground shaking characteristics and local construction practices