Convergence analysis of contrast source inversion type methods for acoustic inverse medium scattering problems

TL;DR

This paper introduces two new iteratively regularized contrast source inversion methods with $ ext{l}_1$ regularization, providing the first rigorous convergence proof for iterative nonlinear inverse scattering algorithms at fixed frequency.

Contribution

The paper develops and proves the global convergence of two novel IRCSI-type methods with $ ext{l}_1$ regularization for acoustic inverse medium scattering, filling a gap in theoretical understanding.

Findings

Methods demonstrate convergence under weak conditions.

Numerical experiments validate the effectiveness.

Comparable computational complexity to original methods.

Abstract

The contrast source inversion (CSI) method and the subspace-based optimization method (SOM) are first proposed in 1997 and 2009, respectively, and subsequently modified. The two methods and their variants share several properties and thus are called the CSI-type methods. The CSI-type methods are efficient and popular methods for solving inverse medium scattering problems, but their rigorous convergence remains an open problem. In this paper, we propose two iteratively regularized CSI-type (IRCSI-type) methods with a novel $\ell_1$ proximal term as the iteratively regularized term: the iteratively regularized CSI (IRCSI) method and the iteratively regularized SOM (IRSOM) method, which have a similar computation complexity to the original CSI and SOM methods, respectively, and prove their global convergence under natural and weak conditions on the original objective function. To the best of our knowledge, this is the first convergence result for iterative methods of solving nonlinear inverse scattering problems with a fixed frequency. The convergence and performance of the two IRCSI-type algorithms are illustrated by numerical experiments.