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WISE: full-Waveform variational Inference via Subsurface Extensions
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Paper
WISE: full-Waveform variational Inference via Subsurface Extensions
2023
Overview
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.
Publisher
Cornell University Library, arXiv.org
Subject
