Physics-informed conditional diffusion model for generalizable elastic wave-mode separation

TL;DR

This paper introduces a physics-informed conditional diffusion model that effectively separates elastic wave modes by integrating physical constraints, achieving accurate results with reduced computational costs and improved generalizability.

Contribution

The paper presents a novel physics-informed diffusion approach that combines domain physics with neural networks for elastic wavefield separation, enhancing accuracy and efficiency.

Findings

Achieves wavefield separation comparable to traditional methods.

Reduces computational cost compared to conventional numerical solutions.

Demonstrates strong generalization across diverse geological scenarios.

Abstract

Traditional elastic wavefield separation methods, while accurate, often demand substantial computational resources, especially for large geological models or 3D scenarios. Purely data-driven neural network approaches can be more efficient, but may fail to generalize and maintain physical consistency due to the absence of explicit physical constraints. Here, we propose a physics-informed conditional diffusion model for elastic wavefield separation that seamlessly integrates domain-specific physics equations into both the training and inference stages of the reverse diffusion process. Conditioned on full elastic wavefields and subsurface P- and S-wave velocity profiles, our method directly predicts clean P-wave modes while enforcing Laplacian separation constraints through physics-guided loss and sampling corrections. Numerical experiments on diverse scenarios yield the separation results that closely match conventional numerical solutions but at a reduced cost, confirming the effectiveness and generalizability of our approach.