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

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A robust algorithm is proposed to reconstruct the spatial support and the Lame 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.
Publisher
SIAM PUBLICATIONS
Issue Date
2017
Language
English
Article Type
Article
Citation

SIAM JOURNAL ON IMAGING SCIENCES, v.10, no.3, pp.1104 - 1138

ISSN
1936-4954
DOI
10.113716M110318X
URI
http://hdl.handle.net/10203/226746
Appears in Collection
MA-Journal Papers(저널논문)BiS-Journal Papers(저널논문)
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