Three-Component Sparse S Transform

• Published in: IEEE transactions on geoscience and remote sensing Vol. 60; pp. 1 - 7
• Main Authors: Kazemnia Kakhki, Mohsen, Mokhtari, Ahmadreza, Mansur, Webe Joao
• Format: Journal Article
• Language: English
• 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
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2022.3219420

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.