  Full Wave Inversion (FWI) imaging scheme has many applications in
engineering, geoscience and medical sciences. In this paper, a surrogate deep
learning FWI approach is presented to quantify properties of materials using
stress 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 efficient
to obtain global minima responses in these cases. This approach is trained
based on random sampled set of material properties and sampled trials around
local minima, therefore, it requires a forward simulation can handle high
heterogeneity, discontinuities and large gradients. High resolution
Kurganov-Tadmor (KT) central finite volume method is used as forward wave
propagation operator. Using the proposed framework, material properties of 2D
media are quantified for several different situations. The results demonstrate
the feasibility of the proposed method for estimating mechanical properties of
materials with high accuracy using deep learning approaches.
