Abstract
A new stochastic ground motion model for generating a suite of ground motion time history with both temporal and frequency nonstationarities for specified earthquake and site characteristics is proposed based on the wavelet method. This new model is defined in terms of 6 key parameters that characterize the duration, evolving intensity, predominant frequency, bandwidth and frequency variation of the ground acceleration process. All parameters, except for peak ground acceleration (PGA), are identified manually from a database of 2444 recorded horizontal accelerations. The two-stage regression analysis method is used to investigate the inter- and intra-event residuals. For any given earthquake and site characteristics in terms of the fault mechanism, moment magnitude, Joyner and Boore distance and site shear-wave velocity, sets of the model parameters are generated and used, in turn, by the stochastic model to generate strong ground motion accelerograms, which can capture and properly embody the primary features of real strong ground motions, including the duration, evolving intensity, spectral content, frequency variation and peak values. In addition, it is shown that the characteristics of the simulated and observed response spectra are similar, and the amplitude of the simulated response spectra are in line with the predicted values from the published seismic ground motion prediction equations (SGMPE) after a systematic comparison. The proposed method can be used to estimate the strong ground motions as inputs for structural seismic dynamic analysis in engineering practice in conjunction with or instead of recorded ground motions.
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References
Abrahamson N and Silva W (2008), Summary of the Abrahamson & Silva NGA ground-motion relations. Earthq spectra, 24(1): 67–97.
Ameri G, Gallovič F, Pacor F and Emolo A (2009), Uncertainties in strong ground-motion prediction with finite-fault synthetic seismograms: An application to the 1984M 5.7 Gubbio, central Italy, earthquake. Bull Seismol Soc Am, 99(2A): 647–663.
Arroyo D, and Ordaz M (2010), Multivariate Bayesian regression analysis applied to ground-motion prediction equations, part 1: theory and synthetic example. Bull Seismol Soc Am, 100(4): 1551–1567.
Atkinson GM and Silva W (2000), Stochastic modeling of California ground motions. Bull Seismol Soc Am, 90(2): 255–274.
Atkinson GM and Boore DM (2007), Boore-Atkinson NGA ground motion relations for the geometric mean horizontal component of peak and spectral ground motion parameters, Pacific Earthquake Engineering Research Center.
Atkinson GM, Assatourians K, Boore DM, Campbell K and Motazedian D (2009), A guide to differences between stochastic point-source and stochastic finite-fault simulations. Bull Seismol Soc Am, 99(6): 3192–3201.
Berardi R, Jimenez M, Zonno G, and Garcia-Fernandez M (1999, August), Calibration of stochastic ground motion simulations for the 1997 Umbria-Marche, Central Italy, earthquake sequence. In: Proc. 9th Intl. Conf. On Soil Dyn Earthq Eng, SDEE (Vol. 99).
Beresnev IA and GM Atkinson (1997), Modeling finite-fault radiation from the ωn spectrum. Bull Seismol Soc Am, 87(1): 67–84.
Beresnev IA and Atkinson GM (1998), FINSIM–a FORTRAN program for simulating stochastic acceleration time histories from finite faults. Seismol Res Lett, 69(1): 27–32.
Boore DM and Atkinson GM (2008), Ground-motion prediction equations for the average horizontal component of PGA, PGV, and 5%-damped PSA at spectral periods between 0.01 s and 10.0 s. Earthq spectra, 24(1): 99–138.
Boore DM (1983), Stochastic simulation of high-frequency ground motions based on seismological models of the radiated spectra. Bull Seismol Soc Am, 73(6A): 1865–1894.
Boore DM (2000), SMSIM–Fortran programs for simulating ground motions from earthquakes: version 2.0—A revision of OFR 96-80-A, US Geology Survey, Open-File Report 00-509: 1–59.
Boore DM (2003), Simulation of ground motion using the stochastic method, Seismic Motion, Lithospheric Structures, Earthquake and Volcanic Sources: The Keiiti Aki Volume: 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.
Cacciola P and Deodatis G (2011), A method for generating fully non-stationary and spectrum-compatible ground motion vector processes. Soil Dyn Earthq Eng, 31(3): 351–360.
Chiou B and Youngs RR (2008), An NGA model for the average horizontal component of peak ground motion and response spectra. Earthq spectra, 24(1): 173–215.
Edwards B and Fäh D (2013), A stochastic ground-motion model for Switzerland. Bull Seismol Soc Am, 103(1): 78–98.
Frankel A (2009), A constant stress-drop model for producing broadband synthetic seismograms: Comparison with the Next Generation Attenuation relations. Bull Seismol Soc Am, 99(2A): 664–680.
Graves RW and Pitarka A (2004), Broadband time history simulation using a hybrid approach. Proc. 13th World Conf. on Earthq Eng, 1–6.
Guatteri M, Mai PM, Beroza GC and Boatwright J (2003), Strong ground-motion prediction from stochastic-dynamic source models. Bull Seismol Soc Am, 93(1): 301–313.
Hartzell S, Harmsen S, Frankel A and Larsen S (1999), Calculation of broadband time histories of ground motion: Comparison of methods and validation using strong-ground motion from the 1994 Northridge earthquake. Bull Seismol Soc Am, 89(6): 1484–1504.
Huang D and Wang G (2015), Region-Specific Spatial Cross-Correlation Model for Stochastic Simulation of Regionalized Ground-Motion Time Histories. Bull Seismol Soc Am, 105(1): 272–284.
Joyner WB and Boore DM (1993), Methods for regression analysis of strong-motion data. Bull Seismol Soc Am, 83(2): 469–487.
Luco N and Bazzurro P (2007), Does amplitude scaling of ground motion records result in biased nonlinear structural drift responses? Earthq Eng Struct D, 36(13): 1813–1835.
Mai PM and Beroza GC (2002), A spatial random field model to characterize complexity in earthquake slip. J Geophys Res: Solid Earth (1978–2012) 107(B11): ESE 10-1-ESE 10–21.
Motazedian D and Atkinson GM (2005), Stochastic finite-fault modeling based on a dynamic corner frequency. Bull Seismol Soc Am, 95(3): 995–1010.
Olsen KB, Madariaga R and Archuleta RJ (1997), Three-dimensional dynamic simulation of the 1992 Landers earthquake. Science, 278(5339): 834–838.
Ou GB and Herrmann RB (1990), A statistical model for ground motion produced by earthquakes at local and regional distances. Bull Seismol Soc Am, 80(6A): 1397–1417.
Pousse G, Bonilla LF, Cotton F and Margerin L (2006), Nonstationary stochastic simulation of strong ground motion time histories including natural variability: Application to the K-net Japanese database. Bull Seismol Soc Am, 96(6): 2103–2117.
Power M, Chiou B, Abrahamson N and Roblee C (2006), The next generation of ground motion attenuation models (NGA) project: An overview, Proc. 8th National Conf. on Earthq Eng.
Rezaeian S. and Kiureghian A. Der. (2010), Simulation of synthetic ground motions for specified earthquake and site characteristics. Earthq Eng Struct D, 39(10): 1155–1180.
Rovelli A, Caserta A, Malagnini L and Marra F (1994), Assessment of potential strong ground motions in the city of Rome.
Saragoni GR and Hart GC (1974), Simulation of artificial earthquakes. Earthq Eng Struct D, 2(3): 249–267.
Spanos PD, Giaralis A and Politis NP (2007), Time–frequency representation of earthquake accelerograms and inelastic structural response records using the adaptive chirplet decomposition and empirical mode decomposition. Soil Dyn Earthq Eng, 27(7): 675–689.
Trifunac MD and Brady AG (1975), A study on the duration of strong earthquake ground motion. Bull Seismol Soc Am, 65(3): 581–626.
Yamamoto Y and Baker JW (2013), Stochastic model for earthquake ground motion using wavelet packets. Bull Seismol Soc Am, 103(6): 3044–3056.
Yeh CH and Wen Y (1990), Modeling of nonstationary ground motion and analysis of inelastic structural response. Struct Saf, 8(1): 281–298.
Zhao JX, Zhang J, Asano A, Ohno Y, Oouchi T, Takahashi T, Ogawa H, Irikura K, Thio HK and Somerville PG (2006), Attenuation relations of strong ground motion in Japan using site classification based on predominant period. Bull Seismol Soc Am, 96(3): 898–913.
Acknowledgments
The research described in this paper was financially supported by the National Natural Science Foundation of China under Grant Nos. 51378092 and 51121005. Any findings and conclusions in this study are those of the authors and do not necessarily reflect the views of the National Natural Science Foundation.
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Li, Y., Wang, G. An Improved Approach for Nonstationary Strong Ground Motion Simulation. Pure Appl. Geophys. 173, 1607–1626 (2016). https://doi.org/10.1007/s00024-015-1189-4
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DOI: https://doi.org/10.1007/s00024-015-1189-4