Abstract
An improved stochastic finite-fault method was used to simulate the Mw 6.6 earthquake that occurred on August 8, 2017, in Jiuzhaigou, Sichuan, China. A variation of the stochastic finite-fault method, considering the dynamic corner frequency, was used to overcome the corner frequency. It explains the effect of a given slip distribution on the corner frequency of each subfault. A comparison of the response spectra (damping ratio 5%), spectral ratio (simulated values/observed values), and model deviation demonstrated that the improved corner frequency performs well for the whole periods, especially in short periods from 0.2 to 1 s. Similarly, the revised duration model and the site-amplification factor were applied to simulate the ground motion during the Jiuzhaigou earthquake, and then the response spectra were obtained. The duration of the nine near-field station recordings matched well with the simulated ground motions, and the peak ground acceleration (PGA) of most of the stations matched well with the observed PGA. In the short periods (T < 1 s), a small difference is observed between the simulated response spectra and the observed spectra for most of the stations. However, the response spectra were overestimated within the period range (0.05–10 s). Generally, the simulated ground motions obtained by using the improved model matched well with the observed ground motions, which could be considered the basis for earthquake-resistant design during the postdisaster recovery and the structural dynamic analysis of the Jiuzhaigou region.
Similar content being viewed by others
References
Aki K (1967) Scaling law of seismic spectrum. J Geophys Res 72(4):1217–1231
Aki K, Richards P (1980) Quantitative seismology. theory and methods. W. H. Freeman and Company, San Francisco, p 932
Ameri G, Emolo A, Pacor F et al (2011) Ground-motion simulation for the 1980 M6.9 Irpinia earthquake (Southern Italy) and Scenario events. Bull Seismol Soc Am 101(3):1136–1151
Anderson JG, Hough SE (1984) A model for the shape of the Fourier amplitude spectrum of acceleration at high frequency. Bull Seismol Soc Am 74:1969–1993
Atkinson G (2004) Empirical attenuation of ground motion spectral amplitudes in southeastern Canada and the northeastern United States. Bull Seismol Soc Am 94(6):1079–1095
Atkinson GM, Boore DM (1998) Evaluation of models for earthquake source spectra in eastern north America. Bull Seismol Soc Am 88:917–934
Atkinson GM, Boore DM (2006) Earthquake ground-motion prediction equations for eastern north America. Bull Seismol Soc Am 96(6):2181–2205
Bassin C, Laske G, Masters G (2000) The current limits of resolution for surface wave tomography in North America. EOS Trans AGU 81(48):F897
Beresnev IA, Atkinson GM (1997) Modeling finite-fault radiation from the ωn spectrum. Bull Seismol Soc Am 87(1):67–84
Beresnev IA, Atkinson GM (1998a) FINSIM-a FORTRAN program for simulating stochastic acceleration time histories from finite faults. Seism Res Lett 69(1):27–32
Beresnev IA, Atkinson GM (1998b) Stochastic finite-fault modeling of ground motion from the 1994 Northridge, California, earthquake. 1. Validation on rock sites. Bull Seismol Soc Am 88(6):1392–1401
Boore DM (1983) Stochastic simulation of high-frequency ground motions based on seismological models of the radiated spectra. Bull Seismol Soc Am 73(6):1865–1894
Boore DM (2003) Simulation of ground motion using the stochastic method. Pure Appl Geophys 160(3):635–676
Boore DM (2009) Comparing stochastic point-source and finite-source ground-motion simulations: SMSIM and EXSIM. Bull Seismol Soc Am 99(6):3202–3216
Boore DM, Atkinson GM (2007) Boore–Atkinson NGA ground motion relations for the geometric mean horizontal component of peak and spectral ground motion parameters. PEER Report. Pacific Earthquake Engineering Research Center
Boore DM, Thompson EM (2015) Revisions to some parameters used in stochastic-method simulations of ground motion. Bull Seismol Soc Am 105(2A):1029–1041
Brune JN (1970) Tectonic stress and the spectra of seismic shear waves from earthquake. J Geophys Res 75(26):4997–5009
Brune JN (1971) Correction. J Geophys Res 76:5002
Castro RR, Rovelli A, Cocco M et al (2001) Stochastic simulation of strong-motion records from the 26 September 1997(Mw 6), Umbria-Marche (central Italy) earthquake. Bull Seismol Soc Am 91(1):27–39
Causse M, Cotton F, Mai PM (2010) Constraining the roughness degree of slip heterogeneity. J Geophys Res Solid Earth 115:B05304. https://doi.org/10.1029/2009JB006747
Chopra S, Kumar D, Rastogi BK (2010) Estimation of strong ground motions for 2001 Bhuj (Mw 7.6), India earthquake. Pure Appl Geophys 167(11):1317–1330
Dang PF, Liu QF (2020) Stochastic finite-fault ground motion simulation for the Mw 6.7 earthquake in Lushan. China. Nat Hazards 100(3):1215–1241
Eshelby JD (1957) The determination of the elastic field of an ellipsoidal inclusion and related problems. Proc R Soc Lond Ser A 241(1226):376–396
Frankel A (1995) Simulating strong motions of large earthquakes using recordings of small earthquakes: the Loma Prieta mainshock as a test case. Bull Seismol Soc Am 85(4):1144–1160
Ghasemi H, Fukushima Y, Koketsu K et al (2010) Ground-motion simulation for the 2008 Wenchuan, China, earthquake using the stochastic finite-fault method. Bull Seismol Soc Am 100(58):2476–2490
Hartzell SH (1978) Earthquake aftershocks as Green’s functions. Geophys Res Lett 5(1):1–14
Husid RL (1969) Analisis deterremotos: analisigeneral. Rev del IDIEM 8(1):21–42
Kanamori H (1994) The mechanics of earthquakes. Ann Rev Earth Planet Sci 22(1):207–237
Kanamori H, Anderson JG (1975) Theoretical basis of some empirical relations in seismology. Bull Seismol Soc Am 65(5):1073–1095
Keilis Borok V (1959) On estimation of the displacement in an earthquake source and of source dimensions. Ann Geophys-ITALY 12(1):205–214
Lermo J, Chavez GFJ (1993) Site effect evaluation using spectral ratios with only one station. Bull Seismol Soc Am 83(5):1574–1594
Mavroeidis GP, Papageorgiou ASP (2003) A mathematical representation of near-fault ground motions. Bull Seismol Soc Am 95:995–1010
Motazedian D, Atkinson GM (2005) Stochastic finite-fault modeling based on a dynamic corner frequency. Bull Seismol Soc Am 95(3):995–1010
Motazedian D, Moinfar A (2006) Hybrid stochastic finite fault modeling of 2003, M6.5, Bam earthquake(Iran). J Seismol 10(1):91–103
RaghuKanth STG, Kavitha B (2013) Stochastic finite fault modeling of subduction Zone Earthquakes in Northeastern India. Pure Appl Geophys 170(11):1705–1727
Shen WH, Zhong Z, Shi BP (2014) Strong ground motion simulation for the 2013 Lushan Mw 6.6 earthquake, Sichuan, China, based on the inverted and synthetic slip models. Earthq Sci 27(4):377–389
Sun XD, Tao XX, Wang GX (2009) Dynamic corner frequency in source spectral model for stochastic synthesis of ground motion. Acta Seismol Sin 31(5):537–543
Sun JZ, Yu YX, Li YQ (2018) Stochastic finite-fault simulation of the 2017 Jiuzhaigou earthquake in China. Earth Planets Space 70:128. https://doi.org/10.1186/s40623-018-0897-2
Toni M (2017) Simulation of strong ground motion parameters of the 1 June 2013 Gulf of Suez earthquake, Egypt. NRIAG J Astron Geophys 6(1):30–40
Wald DJ, Heaton TH, Hudnut KW (1996) The slip history of the 1994 Northridge, California, earthquake determined from strong-motion, teleseismic, GPS, and leveling data. Bull Seismol Soc Am 86(1):S49–S70
Wang GX, Ding Y, Borcherdt R (2015) Simulation of acceleration field of the Lushan earthquake (Ms7.0, April 20, 2013, China). Eng Geol 189(30):84–97
Wang HW, Ren YF, Wen RZ (2017) Source spectra of the 8 August 2017 Jiuzhaigou Ms7.0 earthquake and the quality factor of the epicenter area. Chin J Geophys 60(10):4117–4123 (in Chinese)
Wen RZ, Wang HW, Ren YF (2015) Estimation of source parameters and quality factor based on generalized inversion method in Lushan earthquake. J Harbin Inst Technol 47(4):0058–0064 (in Chinese)
Yalcinkaya E, Pinar A, Uskuloglu O et al (2012) Selecting the most suitable rupture model for the stochastic simulation of the 1999 lzmit earthquake and prediction of peak ground motions. Soil Dyn Earthq Eng 42:1–16
Zafarani H, Rahimi M, Noorzad A et al (2015) Stochastic simulation of strong-motion records from the 2012 Ahar–Varzaghan dual earthquakes, northwest of Iran. Bull Seismol Soc Am 105(3):1419–1434
Zheng XJ, Zhang Y, Wang RJ (2017) Estimating the rupture process of the 8 August 2017 Jiuzhaigou earthquake by inverting strong-motion data with IDS method. Chin J Geophys 60(11):4421–4430 (in Chinese)
Acknowledgements
This research was undertaken with the support of the National Natural Science Foundation of China under Grant Nos. 51978434 and 51078337, and we are sincerely thankful to David Boore for providing the stochastic finite-fault program code, named as EXSIM_DMB. We are also very grateful for the Web site https://gmt-china.org/example/, which helped us complete the draft. CSMNC provided the recordings about the 2017 Jiuzhaigou earthquake, and PEER database provided the recordings about the 1994 Northbridge earthquake. We sincerely thank the two institutions for providing seismic data. We sincerely thank Zhang Yong, a researcher of Hundred Talents Program of Peking University, for offering the slip model. At last but not least, we want to show our thanks to two anonymous reviewers for their helpful comments and advise.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
1.1 Derivation of the improved corner frequency
1.1.1 Derivation of the improved stress drop
The stress drop constitutes the most significant quantity in the scaling relation of the earthquake. The static stress drop Δσs represents the difference in the state of stress on the fault plane before and after the earthquake. The stress drop is Δσ = \(\Delta \sigma_{0} { - }\Delta \sigma_{1}\), where Δσ0 is the initial stress and \(\Delta \sigma_{1}\) is the final stress.
The stress drop \(\Delta \sigma\) is given by
where \(\tilde{D}\) represents the average (final) slip, \(\tilde{L}\) represents a characteristic rupture dimension, μ denotes shear modulus, which is generally equal to 3.0 × 1011 dyne/cm2, Cs is a nondimensional shape factor dependent on the geometry of the order unity (Kanamori 1994). For circular faults, \(C_{s} = {{7\pi } \mathord{\left/ {\vphantom {{7\pi } {16}}} \right. \kern-0pt} {16}}\) (Eshelby 1957; Keilis Borok 1959). For each subfault, we get:
Assuming that all subfaults are identical, we can obtain the result from the sum of the two sides of Eq. (18):
then, we get:
Combining Eq. (18), we get:
As for the stochastic finite-fault method, the physical meaning of stress drop is close to equating to the static stress drop proposed by Kanamori and Anderson (1975), and the final value of stress drop is the mean value of all subfaults.
In the equation, N stands for the subfault number. nl denotes the subfault number along the length of major fault, and nw denotes the same along the width of a major fault, respectively (nl × nw = N). Combining Eqs. (21) and (22), we obtain:
Bringing Eq. (23) into the static corner frequency, f0 = 4.9E + 6β(Δσ/M0)1/3, the corner frequency exhibited by each subfault can be expressed as follows:
1.1.2 Derivation of the corner frequency
Combined with the definition displacement from a point shear dislocation in a homogeneous flexible space (Aki and Richards 1980, Eq. 4.32), and the seismic moment,\(M\left( t \right) \equiv \mu \bar{u}\left( t \right)A\), the far-field shear wave is defined as:
where \(u\left( {x,t} \right)\) represents the displacement at the spatial point x, ρ and μ are the density and shear modulus, respectively, \(R^{\theta \gamma }\) stands for angular radiation pattern, \(\bar{u}^{'} \left( t \right)\) denotes the time derivative of mean displacement throughout the dislocation plane, and A is the area of the dislocation.
The modulus of the Fourier transform of (25) is (see Beresnev and Atkinson 1997, for detail)
where we introduce the notation \(\omega_{c} = {1 \mathord{\left/ {\vphantom {1 \tau }} \right. \kern-0pt} \tau } = 2\pi f_{c}\), and fc is the corner frequency of the spectrum. An “omega-squared” spectrum, under the less formal development of Brune (1970, 1971), is obtained based on n = 1.
It is possible to define the rising time as the time needed by the average slip to reach a specific fraction x of the final slip, \(\bar{u}\left( \infty \right)\). The most common way regarding the rising time definition is to let x = 0.5. When x = 0.5, we could define \({T \mathord{\left/ {\vphantom {T \tau }} \right. \kern-0pt} \tau } \equiv z,\) then the ω2 model (Beresnev and Atkinson 1997, Eq. 6) could be expressed as follows:
we could get z = 1.68 by Eq. (27) and then
Usually, we suppose the rise time as the time when the average slip reaches half of the final slip,\(\bar{u}\left( \infty \right)\). For disk rupture, the rise time can be expressed in the form of
r and Vr are the radius of circular crack and the rupture velocity, respectively.
Substituting Eq. (28) into (29) yields
For a circular fault, the seismic moment can be expressed as
In the circular crack rupture model, the relationship between stress drop and radius of circular crack (Kanamori and Anderson 1975) is as follows:
By substituting Eq. (32) into (31), we obtain
and then the radius of the fault can be expressed as
Substituting Eq. (34) into (30) yields
By substituting Eq. (23) into (35), we obtain
where β denotes the shear-wave velocity in kilometers per second, Δσ denotes the stress drop in bars, and Moaver stands for the average seismic moment in dyne-cm.
Rights and permissions
About this article
Cite this article
Dang, P., Liu, Q. & Song, J. Simulation of the Jiuzhaigou, China, earthquake by stochastic finite-fault method based on variable stress drop. Nat Hazards 103, 2295–2321 (2020). https://doi.org/10.1007/s11069-020-04083-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11069-020-04083-9