Uncertainty Quantification in Friction Model for Earthquakes using Bayesian inference

Abstract

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.

References in corpus (5)

• Design of convergence criterion for fixed stress split iterative scheme for small strain anisotropic poroelastoplasticity coupled with single phase flow
• System of equations and staggered solution algorithm for immiscible two-phase flow coupled with linear poromechanics
• Two-grid method on unstructured tetrahedra: Applying computational geometry to staggered solution of coupled flow and mechanics problems
• An efficient algorithm for numerical homogenization of fluid filled porous solids: part-I
• A simple framework for arriving at bounds on effective moduli in heterogeneous anisotropic poroelastic solids