Abstract
A multi-layer perceptron (MLP) technique is used to train on the response spectra for various strike angles, dip angles, and rake angles. Fixing the magnitude and depth of the earthquakes, the 3-component ground motion is simulated with the help of SPECFEM3D. The residuals of spectral acceleration as a function of time period, for low-rise to high-rise structures, are found to be free of any trend. The hidden layers in the MLP learn the interdependency of focal mechanism parameters on the response spectrum. The resultant model was checked for attenuation characteristics with respect to distance. Furthermore, the trained MLP also showed a shift in spectral peak due to radiation damping, as expected. This MLP architecture presented in this work can be broadly extended to predict the response spectrum, at bedrock level, for any focal mechanism parameters, i.e., strike, dip, and rake, depending on the velocity model of that region.
Code availability
The open-source code used in this study is freely made available on GitHub by Computational Infrastructure for Geodynamics (CIG).
References
Atkinson GM (2015) Ground-motion prediction equation for small-to-moderate events at short hypocentral distances, with application to induced-seismicity hazards. Bull Seismol Soc Am 105:981–992
Churchland PS, Sejnowski TJ (1992) The computational brain. MIT Press, Cambridge
Cybenko G (1992) Approximation by superpositions of a sigmoidal function. Math Control Signals Syst 5(4):455–455
Dhanya J, Raghukanth STG (2018) Ground motion prediction model using artificial neural network. Pure Appl Geophys 175:1035–1064. https://doi.org/10.1007/s00024-017-1751-3
Douglas J, Edwards B (2016) Recent and future developments in earthquake ground motion estimation. Earth Sci Rev 160:203–219. https://doi.org/10.1016/j.earscirev.2016.07.005
Gallant AR, White H (1988) There exists a neural network that does not make avoidable mistakes. Proceedings of the second annual IEEE conference on neural networks, San Diego, CA. IEEE Press, New York, pp I.657–I.664
Güllü H, Erçelebi E (2007) A neural network approach for attenuation relationships: an application using strong ground motion data from Turkey. Eng Geol 93:65–81. https://doi.org/10.1016/j.enggeo.2007.05.004
Hartzell S, Harmsen S, Frankel A (2010) Effects of 3D random correlated velocity perturbations on predicted ground motions. Bull Seismol Soc Am 100(4):1415–1426
Hornik K (1991) Approximation capabilities of multilayer feedforward networks. Neural Netw 4(2):251–257
Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366
Khosravikia F, Clayton P, Nagy Z (2019) Artificial neural network-based framework for developing ground-motion models for natural and induced earthquakes in Oklahoma, Kansas, and Texas. Seismol Res Lett 90:604–613
Komatitsch D, Tromp J (2002) Spectral-element simulations of global seismic wave propagation—I. Validation. Geophys J Int 149:390–412
Levenberg K (1944) A method for the solution of certain non-linear problems in least squares. Quart Appl Math 2:164–168. https://doi.org/10.1090/qam/10666
Marquardt DW (1963) An algorithm for least-squares estimation of nonlinear parameters. J Soc Ind Appl Math 11(2):431–441
Montalva GA, Bastías N, Rodriguez-Marek A (2017) Ground-motion prediction equation for the Chilean subduction zone. Bull Seismol Soc Am 107:901–911
Moré JJ (1978) The Levenberg-Marquardt algorithm: implementation and theory. In: Watson GA (ed) Numerical analysis. Springer, Heidelberg, pp 105–116. https://doi.org/10.1007/BFb0067690
Morikawa N, Fujiwara H (2013) A new ground motion prediction equation for Japan applicable up to M9 mega-earthquake. J Disaster Res 8:878–888
Raghucharan MC, Somala SN, Rodina S (2019) Seismic attenuation model using artificial neural networks. Soil Dyn Earthq Eng 126:105828. https://doi.org/10.1016/j.soildyn.2019.105828
Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 65(6):386
Sontag ED (1992) Feedback stabilization using two-hidden-layer nets. IEEE Trans Neural Netw 3:981–990
Yenier E, Atkinson GM (2014) Equivalent point-source modeling of moderate-to-large magnitude earthquakes and associated ground-motion saturation effects. Bull Seismol Soc Am 104:1458–1478. https://doi.org/10.1785/0120130147
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The authors thank the two anonymous reviewers for their suggestions and comments, which has significantly improved the manuscript.
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Somala, S.N., Chanda, S., Raghucharan, M.C. et al. Spectral acceleration prediction for strike, dip, and rake: a multi-layered perceptron approach. J Seismol 25, 1339–1346 (2021). https://doi.org/10.1007/s10950-021-10031-2
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DOI: https://doi.org/10.1007/s10950-021-10031-2