Full waveform inversion as training a neural network

Abstract

Full waveform inversion (FWI) is usually solved as a nonlinear least-squares problem to minimize the discrepancy between the recorded signal and the synthetic data by some gradient-based optimization methods. In this paper, we investigate acoustic FWI as training a neural network. We recast the time-domain difference scheme into a forward propagation process of vanilla recurrent neural network (RNN) and then find that the parameters of RNN coincide with the physical parameters in the wave equation. As a result, the FWI problem is resolved as training such a kind of neural network. Some stochastic optimization methods such as the Adam optimizer are applied to update the model parameters. We demonstrate our method numerically with three typical models including the benchmark Marmousi model. The numerical results show that the fast convergence and good velocity inversion result can be achieved.