Spectral acceleration prediction for strike, dip, and rake: a multi-layered perceptron approach

被引:0
|
作者
Surendra Nadh Somala
Sarit Chanda
M. C. Raghucharan
Evgenii Rogozhin
机构
[1] Indian Institute of Technology Hyderabad,
[2] Schmidt Institute of Physics of the Earth Russian Academy of Sciences,undefined
来源
Journal of Seismology | 2021年 / 25卷
关键词
Strike; Dip; Rake; Data driven; Hidden layer; Machine learning;
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中图分类号
学科分类号
摘要
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.
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页码:1339 / 1346
页数:7
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