Infinite-Dimensional Bayesian Inversion for Fault Slip from Surface Measurements


Abstract: Given the inability to directly observe the conditions of a fault line, inversion of parameters describing them has been a subject of practical interest for the past couple of decades. To resolve this under a linear elasticity forward model, we consider Bayesian inference in the infinite-dimensional setting given some surface displacement measurements, resulting in a posterior distribution characterizing the initial fault displacement. We employ adjoint-based gradient computation in order to resolve the underlying partial differential equation constrained optimization problem and take care to leverage both dimensionality reductions in the parameter space and the low-rank nature of the resulting posterior covariance, owing to sparse measurements locations, to do said computation in a scalable manner.