Total variation regularization of the $3$-D gravity inverse problem using a randomized generalized singular value decomposition

TL;DR

This paper introduces a fast, scalable algorithm for 3D gravity inverse problems that employs total variation regularization and a randomized generalized singular value decomposition to efficiently preserve sharp subsurface features.

Contribution

It develops a novel randomized SVD approach within an iterative reweighted least squares framework for large-scale 3D gravity inversion with total variation regularization.

Findings

Effective preservation of sharp discontinuities in subsurface structures.

Reduced computational and memory requirements compared to classical methods.

Demonstrated accuracy and efficiency on synthetic examples.

Abstract

We present a fast algorithm for the total variation regularization of the $3$-D gravity inverse problem. Through imposition of the total variation regularization, subsurface structures presenting with sharp discontinuities are preserved better than when using a conventional minimum-structure inversion. The associated problem formulation for the regularization is non linear but can be solved using an iteratively reweighted least squares algorithm. For small scale problems the regularized least squares problem at each iteration can be solved using the generalized singular value decomposition. This is not feasible for large scale problems. Instead we introduce the use of a randomized generalized singular value decomposition in order to reduce the dimensions of the problem and provide an effective and efficient solution technique. For further efficiency an alternating direction algorithm is used to implement the total variation weighting operator within the iteratively reweighted least squares algorithm. Presented results for synthetic examples demonstrate that the novel randomized decomposition provides good accuracy for reduced computational and memory demands as compared to use of classical approaches.