Full-waveform inversion with adversarial losses via deep learning

Abstract

Full-waveform inversion (FWI) is a powerful technology for determining geophysical properties, nevertheless, with high computational cost and difficulty in convergence. Motivated by the powerful tools available in modern deep learning system to deal with complex non-linear inverse problems, we implement the FWI workflow with deep neural networks and propose the use of two different neural networks with adversarial losses to provide fast and accurate solutions. Specifically, a deep neural network is used to generate simulated seismic data based on the finite-difference method, and another fully connected neural network is used to fit the Wasserstein distance between simulated and observed seismic data. The Wasserstein distance and the L2 metric together form an adversarial loss function. The parallel-layer model and the SEAM model are used to verify the benefits of the proposed method and its performance in the face of seismic data lack low-frequency components and noise interference. Numerical experiments demonstrate that, compared with other algorithms, the adaptive gradient (Adagrad) algorithm performs best. In addition, the Adagrad algorithm with adversarial losses has faster convergence speed and more inverting details than with L2 losses. The proposed method provides reliable inversion results, even when seismic data without low-frequency components. Moreover, the adversarial losses are less sensitive to noise, although still difficult to converge.