Initializing Cross-Gradients Joint Inversion of Gravity and Magnetic Data with a Bayesian Surrogate Gravity Model

Abstract

Cross-gradients joint inversion of gravity and magnetic data is the focus of this work. Cross-gradients are introduced as a constraint in the minimization of a least square functional including the misfits of the available data. We propose to initialize the cross gradients iterations with a surrogate density model. The latter is constructed by means of Bayesian estimation in a low dimensional parameter space. To sample from the posterior, an affine invariant MCMC is also introduced. The proposed methodology is successfully tested on synthetic models consisting of isolated sources.