- Library Home /
- Search Collections /
- Open Collections /
- Browse Collections /
- UBC Theses and Dissertations /
- Curvelet-based migration amplitude recovery
Open Collections
UBC Theses and Dissertations
UBC Theses and Dissertations
Curvelet-based migration amplitude recovery P. Moghaddam, Peyman
Abstract
Migration can accurately locate reflectors in the earth but in most cases fails to correctly resolve their amplitude. This might lead to mis-interpretation of the nature of reflector. In this thesis, I introduced a method to accurately recover the amplitude of the seismic reflector. This method relies on a new transform-based recovery that exploits the expression of seismic images by the recently developed curvelet transform. The elements of this transform, called curvelets, are multi-dimensional, multi-scale, and multi-directional. They also remain approximately invariant under the imaging operator. I exploit these properties of the curvelets to introduce a method called Curvelet Match Filtering (CMF) for recovering the seismic amplitude in presence of noise in both migrated image and data. I detail the method and illustrate its performance on synthetic dataset. I also extend CMF formulation to other geophysical applications and present results on multiple removal. In addition of that, I investigate preconditioning of the migration which results to rapid convergence rate of the iterative method using migration.
Item Metadata
Title |
Curvelet-based migration amplitude recovery
|
Creator | |
Publisher |
University of British Columbia
|
Date Issued |
2010
|
Description |
Migration can accurately locate reflectors in the earth but in most cases
fails to correctly resolve their amplitude. This might lead to mis-interpretation
of the nature of reflector.
In this thesis, I introduced a method to accurately recover the amplitude
of the seismic reflector. This method relies on a new transform-based
recovery that exploits the expression of seismic images by the recently developed
curvelet transform. The elements of this transform, called curvelets,
are multi-dimensional, multi-scale, and multi-directional. They also remain
approximately invariant under the imaging operator.
I exploit these properties of the curvelets to introduce a method called
Curvelet Match Filtering (CMF) for recovering the seismic amplitude in
presence of noise in both migrated image and data.
I detail the method and illustrate its performance on synthetic dataset. I
also extend CMF formulation to other geophysical applications and present
results on multiple removal. In addition of that, I investigate preconditioning
of the migration which results to rapid convergence rate of the iterative
method using migration.
|
Genre | |
Type | |
Language |
eng
|
Date Available |
2010-05-04
|
Provider |
Vancouver : University of British Columbia Library
|
Rights |
Attribution-NonCommercial 3.0 Unported
|
DOI |
10.14288/1.0052987
|
URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
|
Graduation Date |
2010-11
|
Campus | |
Scholarly Level |
Graduate
|
Rights URI | |
Aggregated Source Repository |
DSpace
|
Item Media
Item Citations and Data
Rights
Attribution-NonCommercial 3.0 Unported