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1	Recent results in curvelet-based primary-multiple separation: application to real data Wang, Deli, Saab, Rayan, Yilmaz, Ozgur, Herrmann, Felix J. January 2007 (has links) In this abstract, we present a nonlinear curvelet-based sparsitypromoting 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. primary multiple separation curvelets phase space space-spatial frequency plane compression wavefronts SRME nonlinear signal separation