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Curvelet-based primary-multiple separation from a Bayesian perspective Saab, Rayan; Wang, Deli; Yilmaz, Ozgur; Herrmann, Felix J.
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.
Item Metadata
Title |
Curvelet-based primary-multiple separation from a Bayesian perspective
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Creator | |
Contributor | |
Publisher |
Society of Exploration Geophysicists
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Date Issued |
2007
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Description |
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.
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Extent |
252766 bytes
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Subject | |
Genre | |
Type | |
File Format |
application/pdf
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Language |
eng
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Date Available |
2008-03-11
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Provider |
Vancouver : University of British Columbia Library
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Rights |
All rights reserved
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DOI |
10.14288/1.0107412
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URI | |
Affiliation | |
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.
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Peer Review Status |
Unreviewed
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Scholarly Level |
Graduate; Faculty
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Copyright Holder |
Herrmann, Felix J.
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Aggregated Source Repository |
DSpace
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