Seismic full-waveform inversion based on a physics-driven generative adversarial network
This addresses the initial-model dependence and instability in geophysical imaging for seismic data analysis, representing an incremental improvement over conventional FWI methods.
The paper tackled the problem of full-waveform inversion (FWI) being unstable under complex geological conditions by proposing a physics-driven generative adversarial network method, which achieved superior performance in recovering velocity structures with improved structural similarity and signal-to-noise ratio on benchmark models.
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.