Generative AI for full-waveform variational inference

TitleGenerative AI for full-waveform variational inference
Publication TypePresentation
Year of Publication2024
AuthorsZiyi Yin, Rafael Orozco, Mathias Louboutin, Felix J. Herrmann
Keywordsamortized Bayes, conditional normalizing flows, deep learning, FWI, generative AI, GT, inversion, machine learning, Normalizing flows, SLIM, Summary Statistics, uncertain quantification, Variational Inference

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 Keyyin2024GTwise