Blind separation of multicomponent seismic wavefield using SVD of reduced dimension spectral matrix
This paper presents a blind separation algorithm based on singular value decomposition (SVD) of reduced dimension spectral matrix. Furthermore, a mathematical matrix model of the multicomponent seismic wavefield is developed as a framework for implementing the proposed algorithm. The proposed blind...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2017-01-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157816300180 |
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| Summary: | This paper presents a blind separation algorithm based on singular value decomposition (SVD) of reduced dimension spectral matrix. Furthermore, a mathematical matrix model of the multicomponent seismic wavefield is developed as a framework for implementing the proposed algorithm. The proposed blind separation algorithm organizes the frequency transformed multicomponent seismic wavefield into one long data vector. The blind separation of the desired seismic wavefield is accomplished by projecting the long data vector onto the eigenvectors of the dimensionally reduced spectral matrix according to the energy of the eigenvalues. The proposed algorithm is tested on both synthetic and real multicomponent seismic wavefields. Results show outstanding performance compared to the MC-WBSMF algorithm. Therefore, the computational complexity is reduced by a factor greater than 14,400 and there is an improvement in accuracy of 17.5%. |
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| ISSN: | 1319-1578 |