Multi-parameter modeling and analysis of ground motion amplification in the Quaternary sedimentary basin of the Beijing-Tianjin-Hebei region

Abstract

Basin effect was first described following the analysis of seismic ground motion associated with the 1985 M_W8.1 earthquake in Mexico. Basins affect the propagation of seismic waves through various mechanisms, and several unique phenomena, such as the basin edge effect, basin focusing effect, and basin-induced secondary waves, have been observed. Understanding and quantitatively predicting these phenomena are crucial for earthquake disaster reduction. Some pioneering studies in this field have proposed a quantitative relationship between the basin effect on ground motion and basin depth. Unfortunately, basin effect phenomena predicted using a model based only on basin depth exhibit large deviations from actual distributions, implying the severe shortcomings of single-parameter basin effect modeling. Quaternary sediments are thick and widely distributed in the Beijing-Tianjin-Hebei region. The seismic media inside and outside of this basin have significantly different physical properties, and the basin bottom forms an interface with strong seismic reflections. In this study, we established a three-dimensional structure model of the Quaternary sedimentary basin based on the velocity structure model of the North China Craton and used it to simulate the ground motion under a strong earthquake following the spectral element method, obtaining the spatial distribution characteristics of the ground motion amplification ratio throughout the basin. The back-propagation(BP) neural network algorithm was then introduced to establish a multi-parameter mathematical model for predicting ground motion amplification ratios, with the seismic source location, physical property ratio of the media inside and outside the basin, seismic wave frequency, and basin shape as the input parameters. We then examined the main factors influencing the amplification of seismic ground motion in basins based on the prediction results, and concluded that the main factors influencing the basin effect are basin shape and differences in the physical properties of media inside and outside the basin.