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Curvelet-based primary-multiple separation from a Bayesian perspective

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Title: Curvelet-based primary-multiple separation from a Bayesian perspective
Author: Saab, Rayan; Wang, Deli; Yilmaz, Ozgur; Herrmann, Felix J.
Subject Keywords primary separation;multiple separation;curvelet transform;Gaussian noise;curvelets;separation problem;SRME;Bayesian
Issue Date: 2007
Publicly Available in cIRcle 2008-03-11
Publisher Society of Exploration Geophysicists
Citation: Saab, Rayan, Wang, Deli, Yılmaz, Ozgur, Herrmann, Felix J. 2007. Curvelet-based primary-multiple separation from a Bayesian perspective. SEG International Exposition and 77th Annual Meeting.
Abstract: 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.
Affiliation: Science, Faculty ofEarth and Ocean Sciences, Department of
URI: http://hdl.handle.net/2429/562
Peer Review Status: Unreviewed
Scholarly Level: Graduate

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