Full waveform inversion method based on diffusion model
arXiv:2603.22307v1 Announce Type: new 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 c
arXiv:2603.22307v1 Announce Type: new 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.
Executive Summary
This article proposes a novel full-waveform inversion method based on conditional diffusion model regularization to address the limitations of existing seismic imaging techniques. By incorporating two-dimensional density information as a conditional input into the U-Net network, the method improves the resolution and structural fidelity of inversion results, exhibits stronger stability and robustness, and effectively utilizes density information to constrain the inversion. The proposed method has significant practical application value in seismic imaging and fault detection. The results demonstrate significant improvements over existing unconditional diffusion process-based methods, showcasing the potential of conditional diffusion models in regularizing full-waveform inversion.
Key Points
- ▸ The proposed full-waveform inversion method utilizes a conditional diffusion model to regularize the inversion process.
- ▸ The method incorporates two-dimensional density information as a conditional input into the U-Net network.
- ▸ The results demonstrate significant improvements in resolution, structural fidelity, stability, and robustness compared to existing methods.
Merits
Improved Inversion Results
The proposed method demonstrates significant improvements in resolution, structural fidelity, stability, and robustness compared to existing unconditional diffusion process-based methods.
Practical Application Value
The method has significant practical application value in seismic imaging and fault detection, showcasing its potential for real-world use cases.
Demerits
Complexity of the Method
The proposed method may be computationally complex and require significant computational resources, which could limit its practical implementation.
Limited Generalizability
The method may not generalize well to other types of seismic data or inversion problems, requiring further adaptation and validation.
Expert Commentary
The proposed full-waveform inversion method demonstrates significant potential for improving the accuracy and efficiency of seismic imaging applications. By leveraging conditional diffusion models and incorporating two-dimensional density information, the method addresses key limitations of existing unconditional diffusion process-based methods. While the method may be computationally complex and require significant adaptation for other types of seismic data, its practical application value and implications for policy and regulation make it a compelling contribution to the field. Further research and validation are necessary to fully realize the method's potential.
Recommendations
- ✓ Future research should focus on adapting the proposed method for other types of seismic data and inversion problems to demonstrate its generalizability.
- ✓ The development of more efficient and scalable computational frameworks is necessary to support the practical implementation of the method in real-world applications.
Sources
Original: arXiv - cs.LG
