- Library Home /
- Search Collections /
- Open Collections /
- Browse Collections /
- UBC Faculty Research and Publications /
- Robust curvelet-domain primary-multiple separation...
Open Collections
UBC Faculty Research and Publications
Robust curvelet-domain primary-multiple separation with sparseness constraints Herrmann, Felix J.; Verschuur, Dirk J.
Abstract
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.
Item Metadata
Title |
Robust curvelet-domain primary-multiple separation with sparseness constraints
|
Creator | |
Contributor | |
Publisher |
European Association of Geoscientists & Engineers
|
Date Issued |
2005
|
Description |
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.
|
Extent |
1991973 bytes
|
Subject | |
Genre | |
Type | |
File Format |
application/pdf
|
Language |
eng
|
Date Available |
2008-02-26
|
Provider |
Vancouver : University of British Columbia Library
|
Rights |
All rights reserved
|
DOI |
10.14288/1.0107386
|
URI | |
Affiliation | |
Citation |
Herrmann, Felix J., Verschuur, Dirk J. 2005. Robust curvelet-domain primary-multiple separation with sparseness constraints. EAGE 67th Conference & Exhibition Proceedings.
|
Peer Review Status |
Unreviewed
|
Scholarly Level |
Faculty
|
Copyright Holder |
Herrmann, Felix J.
|
Aggregated Source Repository |
DSpace
|
Item Media
Item Citations and Data
Rights
All rights reserved