day2 pm 01 dreger bbp method review ngaeast -...
TRANSCRIPT
Summary of Ground Mo.on Simula.ons Methods: Finite-‐Source and Point-‐Source
July 15, 2014 NGA-‐East SSHAC Workshop 2
Douglas Dreger (UCB)
Ques.ons for Resource Expert • Summarize ground mo.on simula.on models • Provide an overview of the technical bases for the methods considered.
• What features are common among the methods • What features are specific to a given method? • What models are ready for forward simula.ons applica.on?
• Which models or types of models are most appropriate for what range of magnitude and distance?
• What part of the valida.on is most informa.ve in the assignment of a pass/fail grade?
Models Considered • Point-‐Source Stochas.c
– SMSIM (Boore, 1983, 2005, 2014) • Finite-‐Source
– Stochas.c Finite Fault Method (Silva et al., 1990) – EXSIM (Motazedian and Atkinson, 2005; Atkinson and Assatourians,
2014) – G&P (Graves and Pitarka, 2010, 2014) – SDSU (Mena et al., 2010; Mai et al., 2010; Olsen and Takedatsu, 2014)
– UCSB (Liu et al., 2006; Schmedes, 2010, 2013; Crempien and Archuleta, 2014)
– CSM (Zeng et al., 1994, 1995, 1996; Anderson, 2014)
Models Considered • Point-‐Source Stochas.c
– SMSIM (Boore, 1983, 2005, 2014) • Finite-‐Source
– Stochas.c Finite Fault Method (Silva et al., 1990, 1997) – EXSIM (Motazedian and Atkinson, 2005; Atkinson and Assatourians,
2014) – G&P (Graves and Pitarka, 2010, 2014) – SDSU (Mena et al., 2010; Mai et al., 2010; Olsen and Takedatsu, 2014)
– UCSB (Liu et al., 2006; Schmedes, 2010, 2013; Crempien and Archuleta, 2014)
– CSM (Zeng et al., 1994, 1995, 1996; Anderson, 2014)
Stochas.c Simula.on of Time Histories
1) Employs linear filter theory and simplified propaga.on models to scale and shape random phase .me histories and spectra.
Y M0,R, f( ) = E M0, f( )P R, f( )S f( ) I f( )
From SCEC BBP EXSIM Documentation
Stochas.c Simula.on of Time Histories 2) Empirically calibrated shape and scaling func.ons include:
A) Region specific geometrical spreading
B) Region specific Q C) Site correc.ons D) Applica.on of site
kappa or fmax E) Correc.ons for a
magnitude-‐dependent double corner frequency
Y M0,R, f( ) = E M0, f( )P R, f( )S f( ) I f( )
From SCEC BBP EXSIM Documentation
EXSIM – Stochas.c Simula.on of Time Histories
EXSIM introduces a dynamic corner frequency to model a propaga.ng slip pulse..
EXSIM – Stochas.c Simula.on of Time Histories
Sa is calculated from the .me histories. Bias (ln residual) plots are used to compare the observed and simulated Sa. BBP V13.6 valida.on compared simulated mo.ons for 7 earthquakes recorded at ~40 sta.ons each.
SMSIM – New Generalized Double Corner Frequency Models
A∝M 0 f2 1
1+ f fa( )pfa"
#$%&'
pda
1
1+ f fb( )pfb"
#$%&'
pdb
Mul.plica.ve
A∝M0 f
2 1−ε( )
1+ ffa( )
pfa#
$%
&
'(
pda+
M0 f2ε
1+ ffb( )
pfb#
$%
&
'(
pdb
Addi.ve
fa < fc < fb
fc = 4.906x106β Δσ
M0( )1/3
fb = fa
fcfa( )
2
− 1−ε( )
ε
log fa =C1 fa +C2 fa M −M fa( )logε =C1ε +C2ε M −Mε( )
Boore, Di Alessandro, and Abrahamson, 2014
SMSIM – New Generalized Double Corner Frequency Models
Behavior of Mul.plica.ve Model
Boore, Di Alessandro, and Abrahamson, 2014
SMSIM – New Generalized Double Corner Frequency Models
Behavior of Addi.ve Model
Boore, Di Alessandro, and Abrahamson, 2014
Models Considered • Point-‐Source Stochas.c
– SMSIM (Boore, 1983, 2005, 2014) • Finite-‐Source
– Stochas.c Finite Fault Method (Silva et al., 1990) – EXSIM (Motazedian and Atkinson, 2005; Atkinson and Assatourians,
2014) – G&P (Graves and Pitarka, 2010, 2014) – SDSU (Mena et al., 2010; Mai et al., 2010; Olsen and Takedatsu, 2014)
– UCSB (Liu et al., 2006; Schmedes, 2010, 2013; Crempien and Archuleta, 2014)
– CSM (Zeng et al., 1994, 1995, 1996; Anderson, 2014)
Classes of Finite-‐Fault Simula.on Approaches
• Stochas.c (EXSIM, Silva & Darragh) – U.lizes temporally and spectrally shaped white noise to generate .me series
used to es.mate Sa. – Shape func.ons are empirically calibrated
• Determinis.c (CSM and UCSB) – Applies the representa.on theorem for a fault disloca.on u.lizing Green’s
func.ons for simplified 1D velocity structures – Differences in the methods are in terms of the descrip.on of the kinema.c
source model • Hybrid (G&P and SDSU)
– Applies representa.on theorem for the low frequency determinis.c part – Uses a stochas.c component to characterize high frequency por.on of the
spectrum • G&P uses a stochas.c approach following Boore (1983) & Frankel (1995) • SDSU uses scamering func.ons Zeng et al. (1991, 1993)
Seismic Representa.on Theorem
Displacement discon.nuity across fault
Isotropic elas.c tensor
Unit-‐force Green’s func.on represen.ng source to receiver transfer func.on for prescribed velocity model
Composite Source Model – V13.6 1) U.lizes Green’s func.ons that are
complete in terms of body and surface waves, and near-‐, intermediate-‐ and far-‐field terms.
2) 1D velocity models are used with a frequency independent Q model.
3) The source model is built from a distribu.on of randomly placed point-‐sources in which the distribu.on sa.sfies a Gutenberg-‐Richter rela.onship and radius-‐frequency self-‐similarity.
4) The .ming of the subevents is controlled by a constant velocity rupture front ini.a.ng from a hypocenter.
5) Subevents are allowed to overlap.
Composite Source Model-‐ V13.6 1) U.lizes Green’s func.ons that are
complete in terms of body and surface waves, and near-‐, intermediate-‐ and far-‐field terms.
2) 1D velocity models are used with a frequency independent Q model.
3) The source model is built from a distribu.on of randomly placed point-‐sources in which the distribu.on sa.sfies a Gutenberg-‐Richter rela.onship and radius-‐frequency self-‐similarity.
4) The .ming of the subevents is controlled by a constant velocity rupture front ini.a.ng from a hypocenter.
5) Subevents are allowed to overlap.
1. Informed from dynamic simula.ons 2. U.lizes 1D Green’s func.ons with a
frequency dependent Q(f). a. Layered structure use for f < 2 Hz b. Single crustal layer structure used
for f>= 2 Hz 3. PDF and correla.on func.ons between
key kinema.c parameters are obtained from more than 300 dynamic simula.ons a. Slip b. Rise .me c. Peak .me d. Peak slip rate e. Rupture velocity
4. A random slip model assuming a k-‐2 spa.al distribu.on is used with slip amplitude scaled by empirical rela.on is then itera.vely adjusted to conform to the PDF and correla.on func.ons.
5. A 2d finite-‐difference algorithm is used to determine subfault trigger .mes from the locally prescribed rupture velocity.
UCSB Method – V14.3
Graves & Pitarka Method – V14.3 Computes determinis.c long-‐period mo.ons (T > 1 sec) using 1D Green’s func.ons (frequency independent Q), and a kinema.c model based on empirically calibrated theore.cal forms for kinema.c parameter scaling.
• Fault area/dimensions scale with magnitude (Leonard, 2010)
• Correla.on of slip heterogeneity scales with magnitude (Mai and Beroza, 2002) in which the coefficient of varia.on is set at 0.85
• Average rise .me scales with M00.33 (Somerville et
al., 1999) but is double for z<5 km and z>15 km • Background rupture speed scales with local Vs. • Local rise .me scales with square root of local slip
plus a small random component • Local rupture speed scales with local slip and
M00.33
Short-‐period (T < 1 sec) mo.ons are generated using a stochas.c approach very similar to that used by SMSIM
SDSU Method
U.lizes the same low-‐frequency Green’s func.ons and source generator as Graves and Pitarka (2010). Calculates high frequency stochas.c response from isotropic radia.on scamering func.ons from Zeng et al. (1991, 1993) The low-‐frequency and high-‐frequency and combined at 1 Hz similarly to G&P
Evalua.on of Methods • Evalua.on performed within the computa.onal framework of the BBP and was based on median pseudo specral accelera.on.
• Part A: Event/sta.on specific PSA comparsions • Part B: Comparisons with GMPE
• Eight member evalua.on panel held workshops with modelers, reviewed wrimen documenta.on, and simula.on results from the BBP
• Valida.on report for V13.6 was submimed to SCEC on August 1, 2013.
• Documenta.on of the process and an update for V14.3 was submimed for publica.on in an SRL Special Issue
• Part A: Mean Bias • Combined Goodness of
Fit (CGOF) • Pass 0.35 ln units • Fail 0.70 ln units
• Performance is very good for 5<R<300 km and 0.01 < T < 1 second
• <= 20% of cases exceed failure threshold
• ~80% are bemer than a factor of 2
• ~40% are bemer than the 1.41x pass threshold
CGOF = 12ln data
model( ) +12ln data
model( )
• Part A: Method / GMPE • Pass < 1 • Fail > 1.5
• Performance is very good for 5<R<300 km and 0.01 < T < 3 second
Part A: Distance Metric Ln residual is plomed for each event in discrete distance bins. Ideally the slope is zero. Test: If a slope of zero lies within the 95% confidence of the slope es.mate there is no systema.c distance bias and the method passes. Distance metric is the abs(slope)/95%confidence_slope
Ques.ons for Resource Expert • Summarize ground mo.on simula.on models • Provide an overview of the technical bases for the methods considered. • What features are common among the methods • What features are specific to a given method? • What models are ready for forward simula.ons applica.on?
– SMSIM, EXSIM, G&P, SDSU and UCSB • Which models or types of models are most appropriate for what range
of magnitude and distance? – All V14.3 methods within M range of valida.on (M 4.6 – 7.2) – All V14.3 methods for 0 < R <= (200 or 300) km
• What part of the valida.on is most informa.ve in the assignment of a pass/fail grade?
– Part B defines a rigid pass/fail, however there is no one metric that is perfect for this purpose. – A combina.on of metrics as implemented provides a more comprehensive analysis of the
performance of these complex methods. – Addi.onal metrics should be introduced including Fourier amplitude spectra fits, consistency
with net omega-‐2 spectral shape, component specific measures, and limits of sta.c slip to zero fault distance.
References • Anderson, J. G. (2014). The composite source model for broadband simula.ons of strong ground mo.ons,
submimed to Seism. Res. Lem. • Atkinson, G. M., and K. Assatourians (2014). Implementa.on and valida.on of EXSIM (a stochas.c finite-‐fault
ground-‐mo.on simula.on algorithm) on the SCEC broadband plavorm, submimed to Seism. Res. Lem. • Boore, D. M. (1983). Stochas.c simula.on of high-‐frequency ground mo.ons based on seismological models of
the radiated spectra, Bull. Seism. Soc. Am., 73, 1865-‐1894. • Boore, D. M. (2005). SMSIM – Fortran programs for simula.on ground mo.ons from earthquakes: Version 2.3
– A revision of OFR 96-‐80-‐A, US Geological Survey Open-‐File Report, 00-‐509, revised 15 August 2005, 55pp. • Boore, D. M. (2009). Comparing stochas.c point-‐source and finite-‐source ground-‐mo.on simula.ons: SMSIM
and EXSIM, Bull. Seism. Soc. Am., 99, 3202-‐3216. • Boore, D. M., C. Di Alessandro, and N. A. Abrahamson (2014). A generaliza.on of the double-‐corner-‐frequency
source spectral model and its use in the SCEC BBP valida.on exercise, submimed to Bull. Seism. Soc. Am. • Crempien J. G. F., and R. J. Archuleta (2014). UCSB Method for Broadband Ground Mo.on from Kinema.c
Simula.ons of Earthquakes, submimed to Seism. Res. Lem. • Dreger, D. S., G. C. Beroza, S. M. Day, C. A. Goulet, T. H. Jordan, P. A. Spudich, and J. P. Stewart (2014).
Valida.on of the SCEC broadband plavorm V14.3 simula.on methods using pseudo spectral accelera.on data, submimed to Seism. Res. Lem.
• Dreger, D. S., G. C. Beroza, S. M. Day, C. A. Goulet, T. H. Jordan, P. A. Spudich, and J. P. Stewart (2013). Evalua.on of SCEC Broadband Plavorm Phase 1 Ground Mo.on Simula.on Results, SCEC Report No. X, 33 pages.
• Graves, R., and A. Pitarka (2014). Refinements to the Graves and Pitarka (2010) Broadband Ground Mo.on Simula.on Method, submimed to Seism. Res. Lem.
• Graves, R. W., and A. Pitarka (2010). Broadband Ground-‐Mo.on Simula.on Using a Hybrid Approach, Bull. Seism. Soc. Am., 100, 2095-‐2123, doi:10.1785/0120100057.
• Liu, P. and R. J. Archuleta (2004). A new nonlinear finite fault inversion with 3D Green’s func.ons: Applica.on to 1989 Loma Prieta, California, earthquake, J. Geophys. Res., 109, B02318, doi:10.1029/2003JB002625.
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frequency method with correlated random source parameters, Bull. Seism. Soc. Am., 96(6), 2118-‐2130, doi:10.1785/0120060036.
• Mai, P. M., W. Imperatori, and K. B. Olsen (2010). Hybrid broadband ground-‐mo.on simula.ons: combining long-‐period determinis.c synthe.cs with high-‐frequency mul.ple S-‐toS backscamering, Bull. Seism. Soc. Am., 100, 5A, 2124-‐2142, doi:10.1785/0120080194.
• Mena, B., P. M. Mai, K. B. Olsen, M. D. Purvance, and J. N. Brune (2010). Hybrid broadband ground-‐mo.on simula.on using scamering Green’s func.ons: applica.on to large-‐magnitude events, Bull. Seism. Soc. Am., 100, 5A, doi:10.1785/0120080318.
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