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WISE: Full-waveform variational inference via subsurface extensions
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DOI：10.1190/GEO2023-0744.1.png)
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摘要
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We introduce a probabilistic technique for full-waveform inversion, using 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.
Keyword:
MIGRATION VELOCITY ANALYSIS
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期刊
IF:
3.2
论文数:
8.4K
被引数:
3.3W
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