Large-scale focusing joint inversion of gravity and magnetic data with Gramian constraint

Abstract

A fast algorithm for the large-scale joint inversion of gravity and magnetic data is developed. It uses a nonlinear Gramian constraint to impose correlation between density and susceptibility of reconstructed models. The global objective function is formulated in the space of the weighted parameters, but the Gramian constraint is implemented in the original space, and the nonlinear constraint is imposed using two separate Lagrange parameters, one for each model domain. This combined approach provides more similarity between the reconstructed models. It is assumed that the measured data are obtained on a uniform grid and that a consistent regular discretization of the volume domain is imposed. The sensitivity matrices exhibit a block Toeplitz Toeplitz block structure for each depth layer of the model domain. Forward and transpose operations with the matrices can be implemented efficiently using two dimensional fast Fourier transforms. This makes it feasible to solve for large scale problems with respect to both computational costs and memory demands, and to solve the nonlinear problem by applying iterative methods that rely only on matrix vector multiplications. As such, the use of the regularized reweighted conjugate gradient algorithm, in conjunction with the structure of the sensitivity matrices, leads to a fast methodology for large-scale joint inversion of geophysical data sets. Numerical simulations demonstrate that it is possible to apply a nonlinear joint inversion algorithm, with $L_p$-norm stabilisers, for the reconstruction of large model domains on a standard laptop computer. It is demonstrated, that the p=1 choice provides sparse reconstructed solutions with sharp boundaries, and $p=2$ provides smooth and blurred models. Gravity and magnetic data obtained over an area in northwest of Mesoproterozoic St. Francois Terrane, southeast of Missouri, USA are inverted.