Bayesian inference and Markov chain Monte Carlo based estimation of a geoscience model parameter

TL;DR

This paper develops a Bayesian inference framework using Markov chain Monte Carlo methods to estimate the critical slip distance in fault friction models from seismic acceleration data, addressing scale variability.

Contribution

It introduces a novel inversion approach that accurately estimates fault slip parameters solely from observed seismic acceleration data.

Findings

Effective estimation of critical slip distance from noisy acceleration data

Framework demonstrates robustness across different fault scales

Provides a probabilistic assessment of parameter uncertainty

Abstract

The critical slip distance in rate and state model for fault friction in the study of potential earthquakes can vary wildly from micrometers to few meters depending on the length scale of the critically stressed fault. This makes it incredibly important to construct an inversion framework that provides good estimates of the critical slip distance purely based on the observed acceleration at the seismogram. The framework is based on Bayesian inference and Markov chain Monte Carlo. The synthetic data is generated by adding noise to the acceleration output of spring-slider-damper idealization of the rate and state model as the forward model.