A Joint Sparse Recovery Framework for Accurate Reconstruction of Inclusions in Elastic Media
For researchers in elastic imaging and inverse problems, this provides a novel approach to reconstruct inclusions from sparse measurements, though validation is limited to simulations.
The paper proposes a two-step algorithm for reconstructing the spatial support and Lamé parameters of multiple inclusions in elastic media from few boundary displacement measurements, achieving accurate reconstruction without linearization or iterative Green's function updates. Numerical simulations demonstrate efficacy, with the method being the first tailored for elastic parameter reconstruction from highly under-sampled data.
A robust algorithm is proposed to reconstruct the spatial support and the Lamé 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.