Statistical inversion approach to estimating water content in an aquifer from seismic data

Abstract

This study focuses on developing computational tools to estimate water content in an aquifer from seismic measurements. The poroelastic signature from an aquifer is simulated and methods that use this signature to estimate the water table level and aquifer thickness are investigated. In this work, the spectral-element method is used to solve the forward model that characterizes the propagation of seismic waves. The inverse problem is formulated in the Bayesian framework, so that all uncertainties are explicitly modelled as probability distributions, and the solution is given as summary statistics over the posterior distribution of parameters relative to data. For the inverse problem, we use the Bayesian approximation error method which reduces the overall computational demand. In this study, results in the two-dimensional case with simulated data are presented.