Seismic full-waveform inversion based on a physics-driven generative adversarial network

TL;DR

This paper introduces a physics-driven generative adversarial network approach to full-waveform inversion, improving stability and accuracy in reconstructing complex subsurface velocity models under challenging conditions.

Contribution

It combines deep neural networks with physical seismic constraints and adversarial training to enhance FWI robustness and reduce initial model dependence.

Findings

Effective recovery of complex velocity structures

Superior SSIM and SNR performance

Enhanced stability and robustness in inversion

Abstract

Objectives: Full-waveform inversion (FWI) is a high-resolution geophysical imaging technique that reconstructs subsurface velocity models by iteratively minimizing the misfit between predicted and observed seismic data. However, under complex geological conditions, conventional FWI suffers from strong dependence on the initial model and tends to produce unstable results when the data are sparse or contaminated by noise. Methods: To address these limitations, this paper proposes a physics-driven generative adversarial network-based full-waveform inversion method. The proposed approach integrates the data-driven capability of deep neural networks with the physical constraints imposed by the seismic wave equation, and employs adversarial training through a discriminator to enhance the stability and robustness of the inversion results. Results: Experimental results on two representative benchmark geological models demonstrate that the proposed method can effectively recover complex velocity structures and achieves superior performance in terms of structural similarity (SSIM) and signal-to-noise ratio (SNR). Conclusions: This method provides a promising solution for alleviating the initial-model dependence in full-waveform inversion and shows strong potential for practical applications.