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
Keywordscurvelet transform, Geophysics, Optimization, Processing, SLIM

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.

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Citation Keywang2008GEOPbws