Three-Component Sparse S Transform

In this article, the sparse S transform (ST) is extended to three-component (3C) data and considered in the framework of the sparse inverse theory. The 3C sparse ST is formulated as a constrained optimization where the group sparsity constraint is minimized subject to a data fidelity constraint. The...

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Bibliographic Details
Published inIEEE transactions on geoscience and remote sensing Vol. 60; pp. 1 - 7
Main Authors Kazemnia Kakhki, Mohsen, Mokhtari, Ahmadreza, Mansur, Webe Joao
Format Journal Article
LanguageEnglish
Published New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Constraints
Earthquakes
Group sparsity constraint
Matrices
Optimization
Seismic activity
Seismic data
Signal resolution
sparse S transform (ST)
Task analysis
three-component (3C) data
Time-domain analysis
time-frequency (TF) decomposition
Time-frequency analysis
Transformations (mathematics)
Transforms
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Summary:In this article, the sparse S transform (ST) is extended to three-component (3C) data and considered in the framework of the sparse inverse theory. The 3C sparse ST is formulated as a constrained optimization where the group sparsity constraint is minimized subject to a data fidelity constraint. Then a fast and efficient algorithm based on the alternative split Bregman technique is employed to solve the optimization. Numerical experiments using synthetic and real seismic data show that the proposed 3C sparse ST automatically generates higher resolution time-frequency (TF) maps compared to single-component sparse decompositions, which has application in phase splitting and earthquake analysis.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2022.3219420