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The critical slip distance in rate and state model for fault friction in thestudy of potential earthquakes can vary wildly from micrometers to few metersdepending on the length scale of the critically stressed fault. This makes itincredibly important to construct an inversion framework that provides goodestimates of the critical slip distance purely based on the observedacceleration at the seismogram. The framework is based on Bayesian inferenceand Markov chain Monte Carlo. The synthetic data is generated by adding noiseto the acceleration output of spring-slider-damper idealization of the rate andstate model as the forward model.

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