Revealing Spatial Variations of Earthquake Stress Drop and Peak Ground Acceleration Using a Non-Ergodic Modeling Framework

Abstract

Improving accuracy and reducing uncertainty in ground motion models (GMMs) are crucial for the safe design of infrastructure. Traditional GMMs often oversimplify source complexity, such as stress drop, due to high variability in estimation. This study aims to address this issue by extracting robust spatial variations in stress drop estimates and ground motion residuals. We introduce a non-ergodic modeling framework using Bayesian Gaussian Process regression to analyze data from over 5,000 earthquakes (M2-4.5) in the San Francisco Bay area. Our findings reveal consistent spatial patterns in non-ergodic stress drop and peak ground acceleration (PGA), providing a reliable approach to understanding the spatial distribution of stress drop and its link to regional tectonics. Furthermore, integrating source models derived from the non-ergodic stress drop into GMMs can effectively account for source effect in ground motions and reduce aleatory uncertainty. This study establishes a framework for utilizing stress drop data sets to enhance seismic hazard assessment.