Robust curvelet-domain primary-multiple separation with sparseness constraints

TitleRobust curvelet-domain primary-multiple separation with sparseness constraints
Publication TypeConference
Year of Publication2005
AuthorsFelix J. Herrmann, D. J. Verschuur
Conference NameEAGE Annual Conference Proceedings
KeywordsEAGE, SLIM

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.

Citation Keyherrmann2005EAGErcd1