A Prior Regularized Full Waveform Inversion Using Generative Diffusion Models

Fu Wang, Xinquan Huang*, Tariq A. Alkhalifah

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Full waveform inversion (FWI) has the potential to provide high-resolution subsurface model estimations. However, due to limitations in observation, e.g., regional noise, limited aperture, and band-limited data, it is hard to obtain the desired high-resolution model with FWI. To address this challenge, we propose a new paradigm for FWI regularized by generative diffusion model. Specifically, we pretrain a diffusion model in a fully unsupervised manner on a prior velocity model distribution that represents our expectations of the subsurface and then adapt it to the seismic observations by incorporating the FWI into the sampling process of the generative diffusion models. What makes diffusion models uniquely appropriate for such an implementation is that the generative process retains the form and dimensions of the velocity model. Numerical examples demonstrate that our method can outperform the conventional FWI with only negligible additional computational cost. Even in cases of very sparse observations or observations with strong noise, the proposed method could still reconstruct a high-quality subsurface model. Thus, we can incorporate our prior expectations of the solutions in an efficient manner. We further test this approach on field data, which demonstrates the effectiveness of the proposed method.

Original languageEnglish (US)
Article number4509011
Pages (from-to)1-11
Number of pages11
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume61
DOIs
StatePublished - 2023

Keywords

  • Denoising diffusion probabilistic models (DDPMs)
  • full-waveform inversion (FWI)
  • high-resolution
  • regularization

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • General Earth and Planetary Sciences

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