Title: Generative AI for full-waveform 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 in producing realizations of migration-velocity models conditioned by the data. These models are used to quantify amplitude and positioning effects during subsequent imaging.
Bio: Ziyi “Francis” Yin is a Ph.D. candidate in the School of Computational Science and Engineering at Georgia Tech, advised by Prof. Felix J. Herrmann. His research interest includes solving large-scale inverse problems with Bayesian inference and scientific machine learning, along with applications to seismic imaging, inversion, and time-lapse monitoring.
Presenter’s website: Ziyi “Francis” Yin
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Recording: Zoom Recording (will be available within a week after the seminar)