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The complete electrode model for the inverse problem in electrical impedance tomography for damage detection in concrete is presented. The inverse problem in EIT and regularization required to solve this ill-posed inverse problem is described. Both the deterministic and the statistical regularization approaches are explored particularly Gauss-Newton method, sparsity constraints, and Bayesian inversion using Markov Chain Monte Carlo (MCMC) methods are compared using real data.
This presentation is part of Minisymposium “MS54 - Hybrid Approaches that Combine Deterministic and Statistical Regularization for Applied Inverse Problems (4 parts)”
organized by: Cristiana Sebu (University of Malta) , Taufiquar Khan (Clemson University) .