Unsupervised data-guided uncertainty analysis in imaging and horizon tracking

TitleUnsupervised data-guided uncertainty analysis in imaging and horizon tracking
Publication TypeConference
Year of Publication2020
AuthorsAli Siahkoohi, Gabrio Rizzuti, Mathias Louboutin, Felix J. Herrmann
Conference NameSIAM Texas-Louisiana
Keywordsdeep learning, horizon tracking, McMC, Uncertainty quantification

Imaging typically is the first stage of a sequential workflow, and uncertainty quantification becomes more relevant when applied to subsequent tasks. We propose a Bayesian approach to horizon tracking uncertainty analysis, where we deploy a deep prior instead of adhering to handcrafted priors. By passing samples from the posterior distribution obtained via stochastic gradient Langevin dynamics to an automatic horizon tracker, we are able to incorporate the uncertainty on model parameters into horizon tracking.



Citation Keysiahkoohi2020SIAMTXLAudg