Generative AI for full-waveform variational inference
Title | Generative AI for full-waveform variational inference |
Publication Type | Presentation |
Year of Publication | 2024 |
Authors | Ziyi Yin, Rafael Orozco, Mathias Louboutin, Felix J. Herrmann |
Keywords | amortized Bayes, conditional normalizing flows, deep learning, FWI, generative AI, GT, inversion, machine learning, Normalizing flows, SLIM, Summary Statistics, Uncertainty quantification, Variational Inference |
Abstract | 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. |
URL | https://slim.gatech.edu/Publications/Public/Lectures/GTseminar/2024/yin2024GTwise |
Citation Key | yin2024GTwise |