Bayesian wavefield separation by transform-domain sparsity promotion
Title | Bayesian wavefield separation by transform-domain sparsity promotion |
Publication Type | Journal Article |
Year of Publication | 2008 |
Authors | Deli Wang, Rayan Saab, Ozgur Yilmaz, Felix J. Herrmann |
Journal | Geophysics |
Volume | 73 |
Pagination | 1-6 |
Month | 07 |
Keywords | curvelet 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. |
URL | https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2008/wang08GEObws/wang08GEObws.pdf |
DOI | 10.1190/1.2952571 |
HTML Version | |
Citation Key | wang2008GEOPbws |