Including stress relaxation in point-process model for seismic occurrence

TL;DR

This paper compares physics-based and statistical models of seismic events, showing that incorporating stress relaxation improves the fit of probabilistic models to physical earthquake data.

Contribution

It introduces a method to include stress relaxation in point-process models for seismic occurrence, enhancing their ability to replicate physical earthquake dynamics.

Findings

Optimal statistical models account for stress relaxation effects.

Stress discharge influences aftershock clustering.

Model fit varies with minimum magnitude considered.

Abstract

Physics-based and statistic-based models for describing seismic occurrence are two sides of the same coin. In this article we compare the temporal organization of events obtained in a spring-block model for the seismic fault with the one predicted by probabilistic models for seismic occurrence. Thanks to the optimization of the parameters, by means of a Maximum Likelihood Estimation, it is possible to identify the statistical model which fits better the physical one. The results show that the best statistical model must take into account the non trivial interplay between temporal clustering, related to aftershock occurrence, and the stress discharge following the occurrence of high magnitude mainshocks. The two mechanisms contribute in different ways according to the minimum magnitude considered in the data fitting catalog.