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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
2	Seismic imaging and processing with curvelets Herrmann, Felix J., Hennenfent, Gilles, Moghaddam, Peyman P. January 2007 (has links) In this paper, we present a nonlinear curvelet-based sparsity-promoting formulation for three problems in seismic processing and imaging namely, seismic data regularization from data with large percentages of traces missing; seismic amplitude recovery for subsalt images obtained by reverse-time migration and primary-multiple separation, given an inaccurate multiple prediction. We argue why these nonlinear formulations are beneficial. subsalt sub-salt seismic data regularization seismic amplitude recovery primary multiple separation wave front detection curvelet invariance sparsity CRSI Migration amplitude recovery: wavefront

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

Seismic imaging and processing with curvelets