Bayesian inference and Markov chain Monte Carlo based estimation of critical slip distance parameter in rate and state model for fault friction

Abstract

We present an algorithmic framework to solve an inverse problem using Bayesian inference and Markov chain Monte Carlo sampling. The input of the inverse problem is the acceleration of the slipping seismogenic fault and the output is the probability distribution of the critical slip distance parameter of the rate and state model for fault friction.