Curvelet-based primary-multiple separation from a Bayesian perspective

TitleCurvelet-based primary-multiple separation from a {Bayesian} perspective
Publication TypeConference
Year of Publication2007
AuthorsRayan Saab, Deli Wang, Ozgur Yilmaz, Felix J. Herrmann
Conference NameSEG Technical Program Expanded Abstracts
KeywordsPresentation, SEG, SLIM

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. {\copyright}2007 Society of Exploration Geophysicists


Citation Keysaab2007SEGcbp