A Joint Sparse Recovery Framework for Accurate Reconstruction of Inclusions in Elastic Media

TL;DR

This paper introduces a novel joint sparse recovery framework for accurately reconstructing inclusions and their elastic parameters in elastic media from limited boundary measurements, without iterative Green's function updates.

Contribution

It presents a new non-iterative, sparse recovery-based algorithm for elastic parameter reconstruction using under-sampled data, combining M-SBL and C-SALSA techniques.

Findings

Accurate reconstruction demonstrated through extensive simulations.

First algorithm tailored for elastic media parameter recovery with highly under-sampled data.

Effective joint sparse recovery approach enhances robustness and accuracy.

Abstract

A robust algorithm is proposed to reconstruct the spatial support and the Lam\'e parameters of multiple inclusions in a homogeneous background elastic material using a few measurements of the displacement field over a finite collection of boundary points. The algorithm does not require any linearization or iterative update of Green's function but still allows very accurate reconstruction. The breakthrough comes from a novel interpretation of Lippmann-Schwinger type integral representation of the displacement field in terms of unknown densities having common sparse support on the location of inclusions. Accordingly, the proposed algorithm consists of a two-step approach. First, the localization problem is recast as a joint sparse recovery problem that renders the densities and the inclusion support simultaneously. Then, a noise robust constrained optimization problem is formulated for the reconstruction of elastic parameters. An efficient algorithm is designed for numerical implementation using the Multiple Sparse Bayesian Learning (M-SBL) for joint sparse recovery problem and the Constrained Split Augmented Lagrangian Shrinkage Algorithm (C-SALSA) for the constrained optimization problem. The efficacy of the proposed framework is manifested through extensive numerical simulations. To the best of our knowledge, this is the first algorithm tailored for parameter reconstruction problems in elastic media using highly under-sampled data in the sense of Nyquist rate.