🤖 AI Summary
To address the band-limited and geologically distorted reconstructions in full-waveform inversion (FWI) caused by insufficient seismic observations, this paper proposes a conditional diffusion model-driven FWI framework integrating multiple geological priors. For the first time, well-log curves and geological facies labels are jointly embedded as multimodal conditioning inputs into a diffusion model, coupled with classifier-free guidance to enforce joint geophysical–geological constraints—thereby overcoming the limitations of conventional single-velocity-prior regularization. Evaluated on the OpenFWI benchmark and real marine seismic data, the method significantly improves both inversion accuracy and geological plausibility. It outperforms standard FWI and unconditional diffusion-regularized FWI, demonstrating superior robustness under data-scarce conditions. This work establishes a novel paradigm for high-fidelity subsurface imaging when seismic coverage is limited.
📝 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.