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.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:BVAU.2429/562 |
| Date | January 2007 |
| Creators | Saab, Rayan, Wang, Deli, Yilmaz, Ozgur, Herrmann, Felix J. |
| Publisher | Society of Exploration Geophysicists |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | text |
| Rights | Herrmann, Felix J. |
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