  This work presents a framework to inversely quantify uncertainty in the model
parameters of the friction model using earthquake data via the Bayesian
inference. The forward model is the popular rate- and state- friction (RSF)
model along with the spring slider damper idealization. The inverse model is to
determine the model parameters using the earthquake data as the response of the
RSF model. The conventional solution to the inverse problem is the
deterministic parameter values, which may not represent the true value, and
quantifying uncertainty in the model parameters increases confidence in the
estimation. The uncertainty in the model parameters is estimated by the
posterior distribution obtained through the Bayesian inversion.
