WISE: Full-waveform Inference with Subsurface Extensions

TitleWISE: Full-waveform Inference with Subsurface Extensions
Publication TypePresentation
Year of Publication2023
AuthorsZiyi Yin, Rafael Orozco, Mathias Louboutin, Felix J. Herrmann
Keywordsamortized Bayes, conditional normalizing flows, deep learning, FWI, inversion, machine learning, ML4SEISMIC, Normalizing flows, SLIM, Summary Statistics, uncertain quantification, Variational Inference

Quantifying uncertainty in full-waveform inversion is complex given the large sizes of both the model and data. A previous approach employed a variational inference framework, leveraging reverse-time migration to summarize observed data and approximate the posterior distribution through conditional normalizing flows. While reverse-time migration effectively summarizes the data when the background model is close to the true one, its accuracy diminishes with a less accurate background model. In our study, we suggest utilizing subsurface offset gathers as the summary statistics for the variational inference of full-waveform inversion. These gathers retain all the information in seismic data, even when the background model is cycle-skipped or fails to flatten the gathers. Through a case study on Compass model, we confirm our framework's effectiveness and show that subsurface offset gathers offer a better summary statistic than just reverse-time migration.

Citation Keyyin2023ML4SEISMICwise