Quantifying uncertainty in inverse scattering problems set in layered environments

The attempt to solve inverse scattering problems often leads to optimization and sampling problems that require handling moderate to large amounts of partial differential equations acting as constraints. We focus here on determining inclusions in a layered medium from the measurement of wave fields on the surface, while quantifying uncertainty and addressing the effect of wave solver quality. Inclusions are characterized by a few parameters describing their material properties and shapes. We devise algorithms to estimate the most likely configurations by optimizing cost functionals with Bayesian regularizations and wave constraints. In particular, we design an automatic Levenberg-Marquardt-Fletcher type scheme based on the use of algorithmic differentiation and adaptive finite element meshes for time dependent wave equation constraints with changing inclusions. In synthetic tests with a single frequency, this scheme converges in few iterations for increasing noise levels. To attain a global view of other possible high probability configurations and asymmetry effects we resort to parallelizable affine invariant Markov Chain Monte Carlo methods, at the cost of solving a few million wave problems. This forces the use of prefixed meshes. While the optimal configurations remain similar, we encounter additional high probability inclusions influenced by the prior information, the noise level and the layered structure, effect that can be reduced by considering more frequencies.