We leveraged recent advances in deep learning to design neural networks that outperform traditional methods in certain inversion problems, and take advantage of previously unexploited physical information to achieve good performance in regimes inaccessible to classical tools. We also investigated the question of what our networks do, and showed that for toy problems they are computing a version of the cross-correlation.
