Abstract

In this work, nonstationary ground motion coherency analysis and modeling are performed using wavelet analysis and relevance vector machine regression. Earthquake ground motion data from four events recorded at dense seismograph SMART-1 array in north-south and east-west horizontal directions are used to investigate the lagged coherency behavior. Wavelet transform is used to compute the nonstationary lagged coherency that characterizes the space-time variation of seismic ground motion. Lagged coherency behavior with frequency, distance, and time is examined. It is shown that the lagged coherency is a time-varying quantity. The results implied that the lagged coherency on uniform soil is relatively insensitive to the earthquake events, and depends mainly on time, frequency, and separation distance. Relevance Vector Machines (RVM) regression is used to develop a model for nonstationary lagged coherency that characterizes the space-time variation of seismic ground motion. Earthquake ground motion in the in the north-south and the first principal directions is used to determine the lagged coherency model. The proposed model does not require a fixed parametric functional form. The developed model can estimate the lagged coherency for different separation distances at different time instants and frequencies.

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