DEEP BORN OPERATOR LEARNING FOR REFLECTION TOMOGRAPHIC IMAGING

Recent developments in wave-based sensor technologies, such as ground penetrating radar (GPR), provide new opportunities for imaging underground scenes. From the scattered electromagnetic wave measurements obtained by GPR, the goal is to estimate the permittivity distribution of the underground scenes. However, such problems are highly ill-posed, difficult to formulate, and computationally expensive. In this paper, we propose to use a novel physics-inspired machine learning-based method to learn the wave-matter interaction under the GPR setting. The learned forward model is combined with a learned signal prior to recover the unknown underground scenes via optimization. We test our approach on a dataset of 400 permittivity maps with three layer background, which is challenging to solve using existing methods. We demonstrate via numerical results that our proposed method achieves a 50% improvement in mean squared error over the benchmark machine learning-based solvers for reconstructing the underground scenes.