Richter b-value maps from local moments of seismicity

TL;DR

This paper introduces a Bayesian method to map spatial variations in seismicity, specifically the b-value, by modeling seismicity as a nonstationary Gaussian field informed by geological fault data.

Contribution

It presents a novel Bayesian inversion technique that incorporates geological fault information to estimate local seismicity parameters like the b-value.

Findings

Successfully estimates spatial b-value variations.

Incorporates geological fault data into seismicity modeling.

Provides uncertainty quantification for the estimates.

Abstract

We develop a technique to estimate spatially varying seismicity patterns. It is based on a Gaussian approximation of the underlying Poisson Process. A link function is used to estimate local moments of the seismicity from observed catalogues. These are modeled by a nonstationary Gaussian field. We construct a prior based on the local distribution of seismic faults. This allows us to incorporate geological information into the Bayesian inversion of the observed seismicity. In this paper we limit ourselve to the $b$-value field for which we compute the posterior expectations as well as the uncertainties. The technique however may be applied to other seismically relevant parameters like Omori $c$ and $p$-values.