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Permanent link (DOI): https://doi.org/10.7939/R3930P366

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Robust matrix rank reduction methods for seismic data processing Open Access

Descriptions

Other title
Subject/Keyword
Matrix rank reduction
Seismic data reconstruction
Non-Gaussian noise
Convex optimization
Seismic data denoising
Robust statistics
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Chen, Ke
Supervisor and department
Sacchi, Mauricio (Physics)
Examining committee member and department
Currie, Claire (Physics)
Gu, Yu (Physics)
Dumberry, Mathieu (Physics)
Department
Department of Physics
Specialization
Geophysics
Date accepted
2013-09-24T10:21:20Z
Graduation date
2013-11
Degree
Master of Science
Degree level
Master's
Abstract
An important step of seismic data processing entails signal de-noising. Traditional de-noising methods assume Gaussian noise model and their performance degrades in the presence of erratic (non-Gaussian) noise. This thesis examines the problem of designing reduced-rank noise attenuation algorithms that are resistant to erratic noise. I first introduce a robust matrix factorization based on M-estimate and incorporate it into the formulation of the classical Singular Spectrum Analysis (SSA) algorithm. This new algorithm (Robust SSA) permits to de-noise seismic data that have been contaminated by non-Gaussian noise. I also propose a second Robust SSA algorithm that attacks the data de-noising and reconstruct problems as low-rank matrix recovery problem that is solved by a convex optimization algorithm. The NP-hard rank minimization problem is replaced by its tightest convex relaxation, the nuclear-norm minimization. An augmented Lagrangian method is used to numerically look for the solution that minimizes the cost function.
Language
English
DOI
doi:10.7939/R3930P366
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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File format: pdf (Portable Document Format)
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File title: Robust methods for seismic erratic noise attenuation and data reconstruction, MSc Thesis
File author: Ke Chen
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