Robust Physics-Guided Diffusion for Full-Waveform Inversion
Abstract
We develop a robust physics-guided diffusion framework for full-waveform inversion that combines a score-based generative prior with likelihood guidance computed through wave-equation simulations. We adopt a transport-based data-consistency potential (Wasserstein-2), incorporating wavefield enhancement via bounded weighting and observation-dependent normalization, thereby improving robustness to amplitude imbalance and time/phase misalignment. On the inference side, we introduce a preconditioned guided reverse-diffusion scheme that adapts the guidance strength and spatial scaling throughout the reverse-time dynamics, yielding a more stable and effective data-consistency guidance step than standard diffusion posterior sampling (DPS). Numerical experiments on OpenFWI datasets demonstrate improved reconstruction quality over deterministic optimization baselines and standard DPS under comparable computational budgets.
Cite
@article{arxiv.2603.16393,
title = {Robust Physics-Guided Diffusion for Full-Waveform Inversion},
author = {Jishen Peng and Enze Jiang and Zheng Ma and Xiongbin Yan},
journal= {arXiv preprint arXiv:2603.16393},
year = {2026}
}