Geological and Well prior assisted full waveform inversion using conditional diffusion models

TL;DR

This paper introduces a novel geological and well-log prior-assisted full waveform inversion method using conditional diffusion models, improving seismic imaging accuracy by integrating multi-modal geological information.

Contribution

It presents a new approach combining conditional diffusion models with FWI to incorporate geological and well-log priors, enhancing inversion results beyond traditional methods.

Findings

Outperforms conventional FWI in numerical experiments

Effectively integrates multi-modal geological information

Achieves more realistic seismic velocity models

Abstract

Full waveform inversion (FWI) often faces challenges due to inadequate seismic observations, resulting in band-limited and geologically inaccurate inversion results. Incorporating prior information from potential velocity distributions, well-log information, and our geological knowledge and expectations can significantly improve FWI convergence to a realistic model. While diffusion-regularized FWI has shown improved performance compared to conventional FWI by incorporating the velocity distribution prior, it can benefit even more by incorporating well-log information and other geological knowledge priors. To leverage this fact, we propose a geological class and well-information prior-assisted FWI using conditional diffusion models. This method seamlessly integrates multi-modal information into FWI, simultaneously achieving data fitting and universal geologic and geophysics prior matching, which is often not achieved with traditional regularization methods. Specifically, we propose to combine conditional diffusion models with FWI, where we integrate well-log data and geological class conditions into these conditional diffusion models using classifier-free guidance for multi-modal prior matching beyond the original velocity distribution prior. Numerical experiments on the OpenFWI datasets and field marine data demonstrate the effectiveness of our method compared to conventional FWI and the unconditional diffusion-regularized FWI.