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Learned Diffusion Model Regularization for Full Waveform Inversion
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DOI：10.1111/1365-2478.70188.png)
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Abstract
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The diffusion model (DM) is a class of deep generative frameworks that learn the distribution of complex data by iteratively denoising from stochastic perturbations. Here, we introduce a DM-based regularization strategy for full-waveform inversion (FWI), named DM-FWI, which leverages knowledge embedded in an extensive collection of geologic velocity models to train a generalizable model for applying geological priors in a seamless manner. DM's ability to capture high-dimensional structures and generate geologically plausible samples makes it a powerful prior for inverse problems. Within FWI, the DM serves as a structure-adapted smoother, guiding updates towards solutions consistent with realistic geologic patterns. This implicit regularization improves the geological significance of recovered velocity models while alleviating common cycle-skipping issues. Synthetic and field data experiments demonstrate that DM-FWI stabilizes inversion, enhances convergence and yields subsurface models that are both quantitatively accurate and geologically interpretable.
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