WISER: full-Waveform variational Inference via Subsurface Extensions with Refinements

TitleWISER: full-Waveform variational Inference via Subsurface Extensions with Refinements
Publication TypeConference
Year of Publication2024
AuthorsZiyi Yin, Rafael Orozco, Mathias Louboutin, Felix J. Herrmann
Conference NameInternational Meeting for Applied Geoscience and Energy
KeywordsAmortized Variational Inference, Bayesian inference, CIG, conditional normalizing flows, deep learning, FWI, IMAGE, Imaging, Inverse problems, MVA, RTM, SEG, Summary Statistics, Uncertainty quantification, WISE, WISER

We introduce a cost-effective Bayesian inference method for full-waveform inversion (FWI) to quantify uncertainty in migration-velocity models and its impact on imaging. Our method targets inverse uncertainty due to null-space of the wave modeling operators and observational noise, and forward uncertainty where the uncertainty in velocity models is propagated to uncertainty in amplitude and positioning of imaged reflectivities. This is achieved by integrating generative artificial intelligence (genAI) with physics-informed common-image gathers (CIGs), which greatly reduces reliance on accurate initial FWI-velocity models. In addition, we illustrate the capability of fine-tuning the generative AI networks with frugal physics-based refinements to improve the inference accuracy.

Citation Keyyin2024IMAGEwiser