Estimating Inhomogeneous Spatio-Temporal Background Intensity Functions using Graphical Dirichlet Processes

TL;DR

This paper introduces a Bayesian non-parametric model using graphical Dirichlet processes to estimate evolving inhomogeneous spatio-temporal background seismicity, improving understanding of earthquake occurrence patterns.

Contribution

It presents a novel methodology for modeling spatial and temporal inhomogeneities in seismic background activity using graphical Dirichlet processes.

Findings

Applied to southern Mexico seismic data (2000-2015)

Enhanced modeling of seismicity with inhomogeneous Poisson processes

Quantified uncertainty in seismic background estimation

Abstract

An enhancement in seismic measuring instrumentation has been proven to have implications in the quantity of observed earthquakes, since denser networks usually allow recording more events. However, phenomena such as strong earthquakes or even aseismic transients, as slow slip earthquakes, may alter the occurrence of earthquakes. In the field of seismology, it is a standard practice to model background seismicity as a Poisson process. Based on this idea, this work proposes a model that can incorporate the evolving spatial intensity of Poisson processes over time (i.e., we include temporal changes in the background seismicity when modeling). In recent years, novel methodologies have been developed for quantifying the uncertainty in the estimation of the background seismicity in homogeneous cases using Bayesian non-parametric techniques. This work proposes a novel methodology based on graphical Dirichlet processes for incorporating spatial and temporal inhomogeneities in background seismicity. The proposed model in this work is applied to study the seismicity in the southern Mexico, using recorded data from 2000 to 2015.