WISE: full-Waveform variational Inference via Subsurface Extensions

TitleWISE: full-Waveform variational Inference via Subsurface Extensions
Publication TypeJournal Article
Year of Publication2024
AuthorsZiyi Yin, Rafael Orozco, Mathias Louboutin, Felix J. Herrmann
KeywordsAmortized Variational Inference, Bayesian inference, CIG, conditional normalizing flows, deep learning, FWI, Geophysics, Imaging, Inverse problems, MVA, RTM, Summary Statistics, Uncertainty quantification, WISE

We introduce a probabilistic technique for full-waveform inversion, employing variational inference and conditional normalizing flows to quantify uncertainty in migration-velocity models and its impact on imaging. Our approach integrates generative artificial intelligence with physics-informed common-image gathers, reducing reliance on accurate initial velocity models. Considered case studies demonstrate its efficacy producing realizations of migration-velocity models conditioned by the data. These models are used to quantify amplitude and positioning effects during subsequent imaging.



Citation Keyyin2023wise