A hybrid non-parametric ground motion model of power spectral density based on machine learning

Abstract

In the fields of engineering seismology and earthquake engineering, researchers have predominantly focused on ground motion models (GMMs) for intensity measures. However, there has been limited research on power spectral density GMMs (PSD-GMMs) that characterize spectral characteristics. PSD, being structure-independent, offers unique advantages. This study aims to construct PSD-GMMs using non-parametric machine learning (ML) techniques. By considering 241 different frequencies from 0.1 to 25.12 Hz and evaluating eight performance indicators, seven highly accurate and stable ML techniques are selected from 12 different ML techniques as foundational models for the PSD-GMM. Through mixed effects regression analysis, inter-event, intra-event, and inter-site standard deviations are derived. To address inherent modeling uncertainty, this study uses the ratio of the reciprocal of the standard deviation of the total residuals of the foundational models to the sum of the reciprocals of the total residuals of the seven ML GMMs as weight coefficients for constructing a hybrid non-parametric PSD-GMM. Utilizing this model, ground motion records can be simulated, and seismic hazard curves and uniform hazard PSD can be obtained. In summary, the hybrid non-parametric PSD-GMM demonstrates remarkable efficacy in simulating and predicting ground motion records and holds significant potential for guiding seismic hazard and risk analysis.