DiffusionInv: Prior-enhanced Bayesian Full Waveform Inversion using Diffusion models

TL;DR

DiffusionInv introduces a Bayesian full waveform inversion method that integrates a pretrained diffusion model as a prior, improving subsurface velocity model estimation and uncertainty quantification in seismic imaging.

Contribution

The paper presents a novel diffusion model-based prior for Bayesian FWI, enhancing convergence, reliability, and uncertainty assessment over existing methods.

Findings

Successfully recovers high-resolution velocity models in test cases.

Provides meaningful uncertainty estimates of the inversion results.

Outperforms traditional regularized FWI in incorporating prior knowledge.

Abstract

Full waveform inversion (FWI) is an advanced seismic inversion technique for quantitatively estimating subsurface properties. However, with FWI, it is hard to converge to a geologically-realistic subsurface model. Thus, we propose a DiffusionInv approach by integrating a pretrained diffusion model representation of the velocity into FWI within a Bayesian framework. We first train the diffusion model on realistic and expected velocity model samples, preferably prepared based on our geological knowledge of the subsurface, to learn their prior distribution. Once this pretraining is complete, we fine-tune the neural network parameters of the diffusion model by using an L2 norm of data misfit between simulated and observed seismic data as a loss function. This process enables the model to elegantly learn the posterior distribution of velocity models given seismic observations starting from the previously learned prior distribution. The approximate representation of the posterior probability distribution provides valuable insights into the inversion uncertainty for physics-constrained FWI given the seismic data and prior model information. Compared to regularized FWI methods, the diffusion model under a probabilistic framework allows for a more adaptable integration of prior information into FWI, thus mitigating the multi-solution issue and enhancing the reliability of the inversion result. Compared to data-driven FWI methods, the integration of physical constraints in DiffusionInv enhances its ability to generalize across diverse seismic scenarios. The test results in the Hess and Otway examples demonstrate that the DiffusionInv method is capable of recovering the velocity model with high resolution by incorporating the prior model information. The estimated posterior probability distribution helps us understand the inversion uncertainty.