Recent results in curvelet-based primary-multiple separation: application to real data

TitleRecent results in curvelet-based primary-multiple separation: application to real data
Publication TypeConference
Year of Publication2007
AuthorsDeli Wang, Rayan Saab, Ozgur Yilmaz, Felix J. Herrmann
Conference NameSEG Technical Program Expanded Abstracts
Volume26
Page2500-2504
OrganizationSEG
KeywordsPresentation, SEG, SLIM
Abstract

In this abstract, we present a nonlinear curvelet-based sparsity-promoting formulation for the primary-multiple separation problem. We show that these coherent signal components can be separated robustly by explicitly exploting the locality of curvelets in phase space (space-spatial frequency plane) and their ability to compress data volumes that contain wavefronts. This work is an extension of earlier results and the presented algorithms are shown to be stable under noise and moderately erroneous multiple predictions. {\copyright}2007 Society of Exploration Geophysicists

URLhttps://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/wang07SEGrri/wang07SEGrri.pdf
DOI10.1190/1.2792986
Presentation

https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/wang07S...

Citation Keywang2007SEGrri