Regularization of anisotropic full-waveform inversion with multiple parameters by adversarial neural networks

The anisotropic full-waveform inversion (FWI) is a seis-mic inverse problem for multiple parameters, which aims to simultaneously reconstruct the vertical velocity and the anisotropic parameters of the earth's subsurface. This multi -parameter inverse problem suffers from two issues. First, the objective function of the data fitting is less sensitive to the anisotropic parameters. Second, the crosstalk effect among the different parameters worsens the model update in the iterative inversion. We have developed a method that sta-tistically regularizes the anisotropic FWI using Wasserstein adversarial networks, by penalizing the Wasserstein distance between the distribution of the current model parameters and that of the parameters at the borehole locations. The regu-larizer can mitigate the issues of anisotropic FWI with multi-ple parameters and therefore it also can be applied to other inverse problems with multiple parameters.