Abstract
Joint inversions are now commonly used in the earth sciences. They have been developed to better understand the structure of the earth, since they provide more constraints on the inverted parameters. We propose a new process to simultaneously invert several data sets in order to better image 3D crustal and upper mantle structures. Our inversion uses three kinds of data that present good complementarity: (1) P-wave receiver functions to provide Moho depth variations, (2) teleseismic delay times of P-waves to retrieve velocity anomalies in the crust and the upper mantle, and (3) gravity anomalies to image density variations at the lithospheric scale. We use a stochastic scheme, where receiver functions are first inverted. The interpolated resulting Moho depths are incorporated as a priori information into the joint inversion of teleseismic delay times and gravity anomalies process. Moreover, velocity and density can be linked by empirical relationships, which justifies the joint inversion of those parameters. In our stochastic approach, we perform a model space search for Moho variations, P-velocity, and density structure to find an acceptable fit to the three data sets. In order to preferentially sample the good data fit regions, we chose the neighborhood algorithm of Sambridge to optimistically survey the model space. We model the delay times with 3D raytracing using evenly spaced velocity-density nodes. We present here the first results given by this method on synthetic tests. (C) 2011 Elsevier Ltd. All rights reserved.