Enhancing Seismic Waveform Inversion Using a Three-Step Strategy With Adversarial Neural Networks and Seismic Envelope

Seismic full waveform inversion (FWI) represents a state-of-the-art technique for estimating the parameter model. Conventional FWI faces the challenge of cycle skipping, because it depends on waveform matching for its objective function. This challenge impedes its convergence to a satisfactory model. Recently, FWI based on unsupervised learning has gradually developed and achieved commendable results in inversion. Currently, most methods transform waveform matching into probability distributions within a latent space by neural networks to overcome the problem of cycle skipping. Because this method only retains the characteristic information of seismic data while neglecting the full wavefield information, it adversely affects both the inversion resolution and stability. To address this problem, we propose a three-step inversion strategy that integrates the adversarial networks, envelope information, and traditional FWI. This approach is designed to better utilize full wavefield information, which can rebuild low and high wavenumber components of velocity model. The demonstrations using SEG salt and Hess models, which including the salt structure, show that our proposed method can successfully reconstitute the main structure and background velocity of the salt model due to the introduction of full wavefield information.