Speaker
Description
Ground Penetrating Radar (GPR) provides a non-destructive solution for underground utility mapping. The data acquisition process involves emitting known electromagnetic wave into the subsurface and recording the scattered wave above the ground. The goal for inverse scattering is to estimate the spatial distribution of the electric permittivity of the subsurface based on the received scattered wave.
Fourier Neural Operator (FNO) has been used to model time-domain wave propagations. One main challenge of such learned simulators is the error accumulation during temporal unrolling. In this module, we introduce our modification to the FNO architecture that is inspired by the iterative Born approximation to the frequency-domain integral equation for scattering. We will demonstrate the effectiveness of the learned forward operator by applying it to the inverse scattering problem in a GPR setting.
