- Library Home /
- Search Collections /
- Open Collections /
- Browse Collections /
- UBC Faculty Research and Publications /
- Curvelet-domain multiple elimination with sparseness...
Open Collections
UBC Faculty Research and Publications
Curvelet-domain multiple elimination with sparseness constraints. Herrmann, Felix J.; Verschuur, Eric
Abstract
Predictive multiple suppression methods consist of two main steps: a prediction step, in which multiples are predicted from the seismic data, and a subtraction step, in which the predicted multiples are matched with the true multiples in the data. The last step appears crucial in practice: an incorrect adaptive subtraction method will cause multiples to be sub-optimally subtracted or primaries being distorted, or both. Therefore, we propose a new domain for separation of primaries and multiples via the Curvelet transform. This transform maps the data into almost orthogonal localized events with a directional and spatialtemporal component. The multiples are suppressed by thresholding the input data at those Curvelet components where the predicted multiples have large amplitudes. In this way the more traditional filtering of predicted multiples to fit the input data is avoided. An initial field data example shows a considerable improvement in multiple suppression.
Item Metadata
Title |
Curvelet-domain multiple elimination with sparseness constraints.
|
Creator | |
Contributor | |
Publisher |
Society of Exploration Geophysicists
|
Date Issued |
2004
|
Description |
Predictive multiple suppression methods consist of two main steps: a prediction step, in which multiples are predicted from the seismic data, and a subtraction step, in which the predicted multiples are matched with the true multiples in the data. The last step appears crucial in practice: an incorrect adaptive subtraction method will cause multiples to be sub-optimally subtracted or primaries being distorted, or both. Therefore, we propose a new domain for separation of primaries and multiples via the Curvelet transform. This transform maps the data into almost orthogonal localized events with a directional and spatialtemporal component. The multiples are suppressed by thresholding the input data at those Curvelet components where the predicted multiples have large amplitudes. In this way the more traditional filtering of predicted multiples to fit the input data is avoided. An initial field data example shows a considerable improvement in multiple suppression.
|
Extent |
1170084 bytes
|
Subject | |
Genre | |
Type | |
File Format |
application/pdf
|
Language |
eng
|
Date Available |
2008-02-21
|
Provider |
Vancouver : University of British Columbia Library
|
Rights |
All rights reserved
|
DOI |
10.14288/1.0107388
|
URI | |
Affiliation | |
Citation |
Herrmann, Felix J., Verschuur, Eric. Curvelet-domain multiple elimination with sparseness constraints. 2004. SEG Technical Program Expanded Abstracts. 23, 1333-1336.
|
Publisher DOI |
10.1190/1.1851110
|
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