Full waveform inversion method based on diffusion model

2026-03-18

AI Summary

Full-waveform inversion (FWI) is highly nonlinear and sensitive to the initial model, often converging to local minima while neglecting the physical coupling between parameters such as velocity and density. To address these limitations, this work proposes a FWI method regularized by a conditional diffusion model, which— for the first time—incorporates two-dimensional density information as a conditional input into an enhanced U-Net backbone network to explicitly model the physical coupling between velocity and density. This approach significantly improves the resolution, structural fidelity, and robustness of inversion results, demonstrating superior stability and practical applicability in complex geological scenarios.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Search and Optimization: Mixed Discrete/Continuous Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Content-based information diffusion
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Seismic full-waveform inversion is a core technology for obtaining high-resolution subsurface model parameters. However, its highly nonlinear characteristics and strong dependence on the initial model often lead to the inversion process getting trapped in local minima. In recent years, generative diffusion models have provided a way to regularize full-waveform inversion by learning implicit prior distributions. However, existing methods mostly use unconditional diffusion processes, ignoring the inherent physical coupling relationship between velocity and density and other physical properties. This paper proposes a full-waveform inversion method based on conditional diffusion model regularization. By improving the backbone network structure of the diffusion model, two-dimensional density information is introduced as a conditional input into the U-Net network. Experimental results show that the full-waveform inversion method based on the conditional diffusion model significantly improves the resolution and structural fidelity of the inversion results, and exhibits stronger stability and robustness when dealing with complex situations. This method effectively utilizes density information to constrain the inversion and has good practical application value. Keywords: Deep learning; Diffusion model; Full waveform inversion.

Problem

• Full waveform inversion
• Diffusion model
• Local minima
• Physical coupling
• Conditional generation

Innovation

• conditional diffusion model
• full waveform inversion
• density conditioning
• U-Net backbone
• physical prior regularization