Comparison of Compressed Sensing Algorithms for Inversion of 3-D Electrical Resistivity Tomography

Peddinti, Srinivasa Rao and Ranjan, S and K B V N, Phanindra (2016) Comparison of Compressed Sensing Algorithms for Inversion of 3-D Electrical Resistivity Tomography. In: AGU Fall Meet, 12-16 December 2016, USA.

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Image reconstruction algorithms derived from electrical resistivity tomography (ERT) are highly non-linear, sparse, and ill-posed. The inverse problem is much severe, when dealing with 3-D datasets that result in large sized matrices. Conventional gradient based techniques using L2 norm minimization with some sort of regularization can impose smoothness constraint on the solution. Compressed sensing (CS) is relatively new technique that takes the advantage of inherent sparsity in parameter space in one or the other form. If favorable conditions are met, CS was proven to be an efficient image reconstruction technique that uses limited observations without losing edge sharpness. This paper deals with the development of an open source 3-D resistivity inversion tool using CS framework. The forward model was adopted from RESINVM3D (Pidlisecky et al., 2007) with CS as the inverse code. Discrete cosine transformation (DCT) function was used to induce model sparsity in orthogonal form. Two CS based algorithms viz., interior point method and two-step IST were evaluated on a synthetic layered model with surface electrode observations. The algorithms were tested (in terms of quality and convergence) under varying degrees of parameter heterogeneity, model refinement, and reduced observation data space. In comparison to conventional gradient algorithms, CS was proven to effectively reconstruct the sub-surface image with less computational cost. This was observed by a general increase in NRMSE from 0.5 in 10 iterations using gradient algorithm to 0.8 in 5 iterations using CS algorithms.

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Item Type: Conference or Workshop Item (Paper)
Subjects: Civil Engineering
Divisions: Department of Civil Engineering
Depositing User: Team Library
Date Deposited: 23 May 2019 08:52
Last Modified: 23 May 2019 08:52
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