WISE: full-Waveform variational Inference via Subsurface Extensions
Geophysics
2024-04-16 v1 Artificial Intelligence
Machine Learning
Signal Processing
Applications
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.
Cite
@article{arxiv.2401.06230,
title = {WISE: full-Waveform variational Inference via Subsurface Extensions},
author = {Ziyi Yin and Rafael Orozco and Mathias Louboutin and Felix J. Herrmann},
journal= {arXiv preprint arXiv:2401.06230},
year = {2024}
}