Bayesian wavefield separation by transform-domain sparsity promotion

TitleBayesian wavefield separation by transform-domain sparsity promotion
Publication TypeJournal Article
Year of Publication2008
AuthorsDeli Wang, Rayan Saab, Ozgur Yilmaz, Felix J. Herrmann
JournalGeophysics
Volume73
Number5
Page1-6
Month07
Keywordscurvelet transform, Geophysics, Optimization, Processing, SLIM
Abstract

Successful removal of coherent noise sources greatly determines the quality of seismic imaging. Major advances were made in this direction, e.g., Surface-Related Multiple Elimination (SRME) and interferometric ground-roll removal. Still, moderate phase, timing, amplitude errors and clutter in the predicted signal components can be detrimental. Adopting a Bayesian approach along with the assumption of approximate curvelet-domain independence of the to-be-separated signal components, we construct an iterative algorithm that takes the predictions produced by for example SRME as input and separates these components in a robust fashion. In addition, the proposed algorithm controls the energy mismatch between the separated and predicted components. Such a control, which was lacking in earlier curvelet-domain formulations, produces improved results for primary-multiple separation on both synthetic and real data.

URLhttps://www.slim.eos.ubc.ca/Publications/Public/Journals/Geophysics/2008/wang08GEObws/wang08GEObws.pdf
DOI10.1190/1.2952571
HTML version

https://www.slim.eos.ubc.ca/Publications/Private/Journals/2008/wang08TRb...

Citation Keywang2008GEOPbws