Poroelastic full-waveform inversion as training a neural network

In this paper, we investigate the full-waveform inversion (FWI) for recovering three media parameters of the poroelastic wave equations as training a neural network. We recast the poroelastic wave simulation in the time domain by the staggered-grid schemes into a process of recurrent neural networks (RNNs). Furthermore, the parameters of RNNs coincide with the inverted parameters in FWI. The algorithm of FWI with a stochastic gradient optimizer named Adam is proposed. The gradients of the objective function with respect to the media parameters are computed by the automatic differentiation. FWI is implemented numerically for three media parameters, i.e., solid density, Lamé parameter of of saturated matrix and shear modulus of dry porous matrix. The numerical computations with two designed models show the good imaging ability of the described method in this paper. It can be applied to invert more media parameters of the poroelastic wave equations.