WISE: full-Waveform variational Inference via Subsurface Extensions

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.

References in corpus (3)

• An introduction to variational inference in Geophysical inverse problems
• Probabilistic Bayesian optimal experimental design using conditional normalizing flows
• InvertibleNetworks.jl: A Julia package for scalable normalizing flows

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