Title | Bayesian wavefield separation by transform-domain sparsity promotion |
Publication Type | Journal Article |
Year of Publication | 2008 |
Authors | Wang D, Saab R, Yilmaz O, Herrmann FJ |
Journal | Geophysics |
Volume | 73 |
Pagination | 1-6 |
Date Published | 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://www.slim.eos.ubc.ca/Publications/Public/Journals/Geophysics/2008/wang08GEObws/wang08GEObws.pdf |
DOI | 10.1190/1.2952571 |