Paper ID: 2401.06230

WISE: full-Waveform variational Inference via Subsurface Extensions

Ziyi Yin, Rafael Orozco, Mathias Louboutin, Felix J. Herrmann

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.

Submitted: Dec 11, 2023