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SCEC Broadband Platform (BBP) Simulation Methods Validation for NGA-East
BBP Validation Team: N. Abrahamson, P. Somerville, F. Silva, P. Maechling, R. Archuleta, J. Anderson, K. Assatourians, G. Atkinson, J. Bayless, J. Crempien, C. Di Alessandro, R. Graves, T. Hyun, R. Kamai, K. Olsen, R. Takedatsu, F. Wang, K. Wooddell,, D. Dreger, G. Beroza, S. Day, T. Jordan, P. Spudich, J. Stewart and their collaborators…
BBP_v14.3 Validation Exercise
Menu du jour n Introduction n Validation framework and schemes
n Part A. Validation against recorded ground motions n Event and record selection n Correcting for site conditions
n Part B. Validation against GMPEs n Evaluation
BBP_v14.3 Validation Exercise
Large collaborative validation of simulations using the SCEC BroadBand Platform
Driven by need of seismic hazard projects to supplement recorded datasets n South-Western U.S. utilities (SWUS) n PEER NGA-East project (new CENA hazard model) n PEER NGA-West projects
n Southern California Earthquake Center (SCEC) BroadBand Platform (BBP) n Set of computational tools for ground motion
simulations, including post-processing
Collaboration of SWUS-SCEC-PEER critical to success.
3
BBP_v14.3 Validation Exercise
Past validations:
Source: Graves and Pitarka (2010)
4
This exercise: Quantitative validation for forward simulations in engineering problems
BBP_v14.3 Validation Exercise
Objective relevant to NGA-East n Quantitative validation for forward
simulations n To supplement recorded data for development
of GMPEs and hazard analyses n Key focus: 5% damped elastic “average” PSA
(f=0.1-100 Hz/ T=0.01-10 s) n Validation for mean ground motions only –
not for standard deviation!
5
BBP_v14.3 Validation Exercise
Key lessons learned – past validations n Need more transparency... n Need to validate against many events n Need clear documentation of fixed and optimized
parameters from modelers for each region n Need source description that is consistent
between methods n Use unique crustal structure (V, Q) for all models n Consider multiple source realizations n Run simulations for reference site conditions –
correct data with empirical site factors n Make all validation metrics computation and plots
in uniform units/format – implement post-processing pipeline on BBP
n Need to tie-in to specific code/BBP version 6
BBP_v14.3 Validation Exercise
Validation schemes n A. Validation against recorded earthquake
ground motions
n B. Validation against GMPE for generic scenarios
Validation allows for development of region-specific rules (source scaling, path)
7
BBP_v14.3 Validation Exercise
Obs, TS, Surf. Sims, TS, Ref.
Site factors
Obs, IM, Ref.
GOF, Ref.
GMPE, IM, Ref.
Sims, IM, Ref.
IM Processors
Obs, IM, Surf.
IM Processors
Event and stations
Obs: observed/recorded Sims: simulated TS: time series IM: Intensity measures (e.g. PSA) Ref.: reference rock site Surf.: surface, soil site GOF: goodness-of-fit
This validation exercise
Part A.
Part B.
8
BBP_v14.3 Validation Exercise 9
Method Name(s) Method type – Finite fault models Contact(s) and Institution
Composite Source Model (CSM) Broadband deterministic
J. Anderson (UNR)
UCSB R. Archuleta, J. Crempien
(UCSB)
EXSIM Stochastic Brune spectrum K. Assatourians, G. Atkinson (UWO)
Graves and Pitarka Hybrid: deterministic LF and stochastic HF
R. Graves (USGS)
SDSU (BB Toolbox) K. Olsen (SDSU)
Simulation Methods and Modelers
Point source methods validated outside BBP: SMSIM Silva’s stochastic models (also FF)
GMPEs used for comparison: NGA-West1: As, BA, CB, CY (2008) CENA: Silva SCVS 2003, Atkinson 2008-2001, Pezeshk et al. (2011)
BBP_v14.3 Validation Exercise
Selection of events and stations Part A (comparison with recordings)
§ Large dataset (~25 EQs)
§ Many regions & tectonic environments
§ Span wide magnitude range
(Mw 4.64 to 7.62)
§ Variety of mechanisms
§ Well-recorded (17 EQs with> 40 records)
§ Select a large subset of stations (~40) that are consistent with mean and standard deviation PSa of the full dataset.
10
Region Event Name Year Mw # Records < 200 km (* <1000 km)
Note on Selection # Selected Records
Actual (final)
WUS Loma Prieta 1989 59 40
WUS Northridge 1994 6.73 124 All stations within 10 km selected 39
WUS Landers 1992 7.22 69 All stations within 100 km selected 40
WUS Whittier Narrows 1987 5.89 95 Truncate stations
at 40 km 39
WUS North Palm Springs 1986 6.12 32 31
JAPAN Tottori 2000 6.59 171 40
JAPAN Niigata 2004 6.65 246 40
WUS Alum Rock 5.45 40 40
WUS Chino Hills 5.39 40 40
CENA Saguenay 1988 5.81 14* All records
selected, use only within 200 km
11
CENA Riviere-du-Loup 2005 4.6 98*
All records selected, use only
within 200 km 21
CENA Mineral, VA 2011 5.68 94* All records
selected, use only within 300 km
10
BBP_v14.3 Validation Exercise
Selection of stations GOAL: Reduce the number of records (stations) per event while maintaining the mean and standard deviation of the full dataset. CRITERIA: § Site Vs30 >= 300 m/s to reduce strong non-linear site
effects § Well-Recorded Earthquakes (40 stations per. event is ideal) § Close-in records (<200 km; <1000 km for CENA)
METHODOLOGY: 1. Develop simple regression model for all stations for each
earthquake (PGA, T=0.2 sec, T=1.0 s) 2. Randomly sample 10 stations from 4 equal distance bins
(log space) to generate ~1000 to 10,000 random samples. 3. Perform regressions on samples from population. 4. Develop a penalty function to select station sample with
small mean bias and standard deviation over all spectral periods (PGA, T=0.2 sec, T=1.0 sec)
Part A (comparison with recordings)
BBP_v14.3 Validation Exercise
Selection of stations Northridge Example
Part A (comparison with recordings)
−120 −119.5 −119 −118.5 −118 −117.5 −117 −116.533.6
33.8
34
34.2
34.4
34.6
34.8
35
35.2
35.4
Longitude
Latit
ude
Northridge Station Locations
101 102102
103
Distance (km)
Vs30
Northridge
Vs30 Full SetSampled Stations (Vs30 > 300 m/s)
300 m/s
103
102
BBP_v14.3 Validation Exercise
Selection of stations Northridge Example
Part A (comparison with recordings)
100 101 10210−4
10−3
10−2
10−1
100
Distance (km)
PGA
(g)
Northridge
Full Model PredictionSample PredictionPGA ObservationsRandom Samples
σfull = 0.442 σselected= 0.444
100 101 10210−4
10−3
10−2
10−1
100
Distance (km)
Spec
tral A
ccel
erat
ion
at T
=0.2
sec
(g)
Northridge
Full Model PredictionSample PredictionT=0.2 sec ObservationsRandom Samples
100 101 10210−4
10−3
10−2
10−1
100
Distance (km)
Spec
tral A
ccel
erat
ion
at T
=1.0
sec
(g)
Northridge
Full Model PredictionSample PredictionT=1.0 sec ObservationsRandom Samples
100
10-4
10-2
σfull = 0.494 σselected= 0.505
σfull = 0.474 σselected= 0.476
BBP_v14.3 Validation Exercise
Correcting data to reference condition
n WUS: Recorded data corrected to Vs30=863 m/s n Empirical amplification factors from Stewart
and Seyhan (Boore et al. NGA-West2 model) n Flin=Linear scaling (Vs30 effect) n Fnl=Nonlinear scaling relative to Flin
n Fsite=1/exp(Flin+Fnl) n Basin correction through Z1.0=f(Vs30) based on
Chiou and Youngs 2008 (NGA-West1 model) n Fbasin=1/exp(f(Φ5,Φ6,Φ7,Φ8)
n Corrected PSA=PSARec*Fsite*Fbasin
Part A (comparison with recordings)
BBP_v14.3 Validation Exercise
Correcting data to reference condition
n CENA: Recorded data corrected to Vs30,ref=1000 m/s
n Empirical amplification based on GWG memo (Stewart et al.) n Flin=Linear scaling (Vs30,rec to 760 m/s) n F760-Vs30,ref= Linear scaling using updated AB06
factors (Boore, personal communication)
n Fsite=1/exp(Flin+F760-Vs30,ref)
n Corrected PSA=PSARec*Fsite
Part A (comparison with recordings)
BBP_v14.3 Validation Exercise
Nomenclature For each scenario, specification of: n Source: Mw, geometry, location, hypocenter n Path: consistent with 1D velocity model n Site (as-recorded to reference): empirical site
correction factors from Boore et al. 2013 NGA-West2
For each scenario, seismograms generated for: n 50 source realizations n ~ 40 stations n 2 horizontal dir.
Part A (comparison with recordings)
4,000 time series
16
BBP_v14.3 Validation Exercise
Ground motion products Part A (comparison with recordings)
17
Northridge PSA, station SCE Vs. 1000034
PSA (
g)
Qualitative evaluation of velocity time series and Husid plot based on Arias intensity
BBP_v14.3 Validation Exercise
Ground motion products
n Average GOF with T for all stations within an event
Part A (comparison with recordings)
18
n Goodness-of-fit measures for PSA
Period (s)
n Average GOF with T for all realizations (all stations)
Period (s)
BBP_v14.3 Validation Exercise
Ground motion products
n Average GOF with distance (all realizations)
Part A (comparison with recordings)
19
n Goodness-of-fit measures for PSA
BBP_v14.3 Validation Exercise
Ground motion products
n Map of average GOF (all realizations)
Part A (comparison with recordings)
20
n Goodness-of-fit measures for PSA
BBP_v14.3 Validation Exercise
n GOF plots also developed for n NGA-West1
(2008) GMPEs n SMSIM
Allows to see trends/event terms
Part A (comparison with recordings)
Ground motion products
21
BBP_v14.3 Validation Exercise
Part B: Scenario selection n Scenarios from NGA-West1&2 well
constrained by data at 20 and 50 km Rrup n M6.2 SS, Vertical, Ztor= 4 km n M6.6 SS, Vertical, Ztor= 0 km n M6.6 REV, Dip=45 deg., Ztor= 3 km n M5.5 REV, Dip=45 deg., Ztor= 6 km
n 50 realizations of the source, WITH randomized hypocenter location for each
n Simulations for two velocity models: NorCal and SoCal
Part B (comparison with GMPEs)
22
BBP_v14.3 Validation Exercise
Summary of Simulated Events
Tottori
Niigata
Chino Hills
Landers
Loma Prieta
Northridge
Alum Rock
* Part B: 4 scenarios
Summary - Parts A and B
23
Saguenay Mineral
Riviere-du-Loup
Whittier
North Palm Springs
BBP_v14.3 Validation Exercise
Evaluation n Review panel (June 2013)
n Douglas Dreger (Chair), UC Berkeley n Gregory Beroza, Stanford n Steven Day, SDSU n Christine Goulet, UC Berkeley n Thomas Jordan, USC n Paul Spudich, USGS n Jonathan Stewart, UCLA
n Input for review n Modeler’s documentation and self-assessment n BBP results (parts A and B)
n Part A: criteria based on binned GOF according to M (event), R, T
n Part B: simple pass-fail
Evaluation
24
BBP_v14.3 Validation Exercise
Evaluation – Part A 1. Comparison of PSA GOF for each event
Mean bias Mean absolute bias
n Failure threshold is ln(2)=0.69 n Thresholds of 0.5 and 0.35 were considered as passing
criteria
Evaluation Part A
25
70-2
00 k
m
27
0.01 to 0.1 s 0.1 to 1 s 1 to 3 s More than3 s
0-5
km
5-20
km
20
-70
km
70-2
00 k
m
BBP_v14.3 Validation Exercise
Evaluation – Part A 1. Comparison of PSA GOF (mean and mean absolute bias) 2. Combined metric: mean and mean absolute bias
n Used alone n Used with GMPEs
3. Evaluation of attenuation bias
n Distance dependence slope of zero within 95% confidence interval
Evaluation Part A
28
BBP_v14.3 Validation Exercise
Combined Metric & Comparison with GMPEs
CGOF = 12ln data
model( ) +12ln data
model( )
Evaluation Part A
29
CGOF
30
0-5
km
5-20
km
20
-70
km
70-2
00 k
m
BBP_v14.3 Validation Exercise
Combined Metric & Comparison with GMPEs
CGOF = 12ln data
model( ) +12ln data
model( )
Evaluation Part A
31
CGOFNormalized =CGOFsims CGOFGMPE
CGOFNormalized
32
0-5
km
5-20
km
20
-70
km
70-2
00 k
m
BBP_v14.3 Validation Exercise
Evaluation – Part A 1. Comparison of PSA GOF (mean and mean absolute bias) 2. Combined metric: mean and mean absolute bias
n Used alone n Used with GMPEs
3. Evaluation of attenuation bias n Distance dependence slope of zero within 95%
confidence interval n Also check systematic bias (qualitative)
Evaluation Part A
33
ln SaobsSasyn
!
"#
$
%&= a+ b ⋅ ln R( )
Fit a line through distance binned GOF values
Determine whether slope b=0 lies within 95% confidence interval
Attenuation Bias Evaluation Part A
34
BBP_v14.3 Validation Exercise
Bias with distance
Red shows a ratio of abs(b)/b95% greater than 1.0, the zero slope does not lie within the 95% CI from simulations. Green shows cases where b=0 lies within the 95% CI from simulations. Smaller numbers are generally controlled by small estimates of slope.
BBP_v14.3 Validation Exercise
Part B – Design and Evaluation criteria Part B (comparison with GMPEs)
36
n Scenarios from NGA-West1&2 well constrained by data at 20 and 50 km Rrup n M5.5 REV n M6.2 SS n M6.6 SS & REV
n 50 realizations of the source, WITH randomized hypocenter location for each
n Simulations for two velocity models: NorCal and SoCal
BBP_v14.3 Validation Exercise
v14.3 CG Update of Review Panel Findings n Details in SRL Focus issue (8 papers) under review n The BBP objective of a version-controlled numerical test bed with
common post-processing tools was successful in producing results enabling straightforward analysis and review.
n All of the currently implemented methods should continue to be refined and improved to provide a variety of options for users and to capture epistemic uncertainty.
n Four methods, EXSIM, G&P, SDSU and UCSB were found to be suitable for simulation of spectral acceleration from T=0.01 to 3 s over R= 0 to 200 km within the validation magnitude range (Mw 5.9-7.2) for WUS events.
n The methods are deemed suitable up to Mw 8 for purposes of assessing relative effects of changes in source geometry, rupture direction, presence of secondary slip on splays, hanging wall effects, etc. Additional work is needed for absolute amplitudes.
n At T>1 s there is increased bias, and for T> 3 s there are significant deviations from GMPEs, based on WUS events.
Evaluation - Results
37
BBP_v14.3 Validation Exercise
References n Atkinson, G. M., D. M. Boore, K. Assatourians, K. Campbell and D. Motazedian (2009). A guide to differences between stochastic
point-source and stochastic finite-fault simulations, Bull. Seism. Soc. Am. 99, 3192-3201. n Beresnev, I., and G. Atkinson (1998a).FINSIM: a FORTRAN program for simulating stochastic acceleration time histories from finite
faults, Seism. Res. Lett. 69, 27–32. n Beresnev, I., and G. Atkinson (1998a).FINSIM: a FORTRAN program for simulating stochastic acceleration time histories from finite
faults, Seism. Res. Lett. 69, 27–32. n Boore, D. M. (2005). SMSIM--Fortran Programs for Simulating Ground Motions from Earthquakes: Version 2.3--A Revision of OFR
96-80-A , U.S. Geological Survey Open-File Report n Boore, D. M. (2009). Comparing stochastic point-source and finite-source ground-motion simulations: SMSIM and EXSIM, Bull.
Seism. Soc. Am. 99, 3202-3216. n Brune, J. N. (1970). Tectonic stress and the spectra of seismic shear waves from earthquakes, J. Geophys. Res., 76, 5002. n Graves, R. A. Pitarka (2010). Broadband ground motion simulation using hybrid approach, Bulletin of Seismological Society of
America. Bull. Seism. Soc. Am., 100, 5A, 2095-2123. n Irikura, K. and H. Miyake (2010). Recipe for Predicting Strong Ground Motion from Crustal Earthquake Scenarios, Pure and Applied
Geophysics, DOI 10.1007/s00024-010-0150-9. n Liu, P., R. J. Archuleta and S. H. Hartzell (2006). Prediction of broadband ground-motion time histories: Hybrid low/high-frequency
method with correlated random source parameters, Bull. Seismol. Soc. Am., 96, 2118-2130, doi: 10.1785/0120060036. n Mai, P.M., Imperatori, W., Olsen, K.B. (2010), Hybrid Broadband Ground-Motion Simulations: Combining Long-Period Deterministic
Synthetics with High-Frequency Multiple S-to-S Backscattering, BSSA, 100(5A), pp. 2124-2142. n Mena, B. and Mai, P. M., Olsen, K. B., Purvance, M. D. and Brune, J. N. (2010). Hybrid Broadband Ground-Motion Simulation Using
Scattering Green's Functions: Application to Large-Magnitude Events, Bull. Seism. Soc. Am. 100, 2143-2162. n Motazedian, D., and G. M. Atkinson (2005). Stochastic finite-fault modeling based on a dynamic corner frequency, Bull. Seismol.
Soc. Am. 95, 995 – 1010. n point source in multilayered media, Geophysical Journal International, Volume 148, Issue 3, pp. 619-627. n Schmedes, J., R. J. Archuleta, and D. Lavallée (2010). Correlation of earthquake source parameters inferred from dynamic rupture
simulations, J. Geophys. Res., 115, B03304, doi:10.1029/2009JB006689. n Schmedes, J., R. J. Archuleta, and D. Lavallée (2010). Dependency of supershear transition and ground motion on the
autocorrelation of initial stress, Tectonophysics, 493, 222-235, doi: 10.1016/j.tecto.2010.05.013 n Schmedes, J., R. J. Archuleta, and D. Lavallée, (2012). A kinematic rupture model generator incorporating spatial interdependency of
earthquake source parameters, Geophys. J. Int., doi: 10.1093/gji/ggs021 n Somerville, P. G., Callaghan, S., Maechling, P., Graves, R. W., Collins, N., Olsen, K. B., Imperatori, W., Jones, M., Archuleta, R.,
Schmedes, J., And Jordan, T.H. (2011). The SCEC Broadband Ground Motion Simulation Platform, SRL, 82(2), p. 275, 10.1785/gssrl.82.2.273.
n Zeng, Y., J. G. Anderson and G. Yu (1994). A composite source model for computing realistic synthetic strong ground motions, Geophysical Research Letters 21, 725-728.
n Zhu, L. & Rivera, L. A. (2002) A note on the dynamic and static displacements from a