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Full Wave Inversion (FWI) imaging scheme has many applications inengineering, geoscience and medical sciences. In this paper, a surrogate deeplearning FWI approach is presented to quantify properties of materials usingstress waves. Such inverse problems, in general, are ill-posed and nonconvex,especially in cases where the solutions exhibit shocks, heterogeneity,discontinuities, or large gradients. The proposed approach is proven efficientto obtain global minima responses in these cases. This approach is trainedbased on random sampled set of material properties and sampled trials aroundlocal minima, therefore, it requires a forward simulation can handle highheterogeneity, discontinuities and large gradients. High resolutionKurganov-Tadmor (KT) central finite volume method is used as forward wavepropagation operator. Using the proposed framework, material properties of 2Dmedia are quantified for several different situations. The results demonstratethe feasibility of the proposed method for estimating mechanical properties ofmaterials with high accuracy using deep learning approaches.

Deep learning surrogate interacting Markov chain Monte Carlo based full wave inversion scheme for properties of materials quantification