WISE: full-Waveform variational Inference via Subsurface Extensions
This addresses uncertainty in subsurface imaging for geophysics, but appears incremental as it builds on existing variational inference and normalizing flow methods.
The paper tackles uncertainty quantification in full-waveform inversion for migration-velocity models by introducing a probabilistic technique using variational inference and conditional normalizing flows, demonstrating efficacy in case studies for quantifying amplitude and positioning effects in imaging.
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.