Global ETD Search

1	Robust curvelet-domain primary-multiple separation with sparseness constraints Herrmann, Felix J., Verschuur, Dirk J. January 2005 (has links) A non-linear primary-multiple separation method using curvelets frames is presented. The advantage of this method is that curvelets arguably provide an optimal sparse representation for both primaries and multiples. As such curvelets frames are ideal candidates to separate primaries from multiples given inaccurate predictions for these two data components. The method derives its robustness regarding the presence of noise; errors in the prediction and missing data from the curvelet frame's ability (i) to represent both signal components with a limited number of multi-scale and directional basis functions; (ii) to separate the components on the basis of differences in location, orientation and scales and (iii) to minimize correlations between the coefficients of the two components. A brief sketch of the theory is provided as well as a number of examples on synthetic and real data. curvelets sparseness constraints primary separation multiple separation
2	Curvelet-based primary-multiple separation from a Bayesian perspective Saab, Rayan, Wang, Deli, Yilmaz, Ozgur, Herrmann, Felix J. January 2007 (has links) In this abstract, we present a novel primary-multiple separation scheme which makes use of the sparsity of both primaries and multiples in a transform domain, such as the curvelet transform, to provide estimates of each. The proposed algorithm utilizes seismic data as well as the output of a preliminary step that provides (possibly) erroneous predictions of the multiples. The algorithm separates the signal components, i.e., the primaries and multiples, by solving an optimization problem that assumes noisy input data and can be derived from a Bayesian perspective. More precisely, the optimization problem can be arrived at via an assumption of a weighted Laplacian distribution for the primary and multiple coefficients in the transform domain and of white Gaussian noise contaminating both the seismic data and the preliminary prediction of the multiples, which both serve as input to the algorithm. primary separation multiple separation curvelet transform Gaussian noise curvelets separation problem SRME Bayesian
3	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
4	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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