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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| Format: | Article |
| Language: | English |
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Springer
2017-01-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157816300180 |
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| author | Abdullah Al-Hasanat Abdelwadood Mesleh Monther Krishan Ahmed Sharadqh Aws Al-Qaisi W.L. Woo S.S. Dlay |
| author_facet | Abdullah Al-Hasanat Abdelwadood Mesleh Monther Krishan Ahmed Sharadqh Aws Al-Qaisi W.L. Woo S.S. Dlay |
| author_sort | Abdullah Al-Hasanat |
| collection | DOAJ |
| description | 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%. |
| format | Article |
| id | doaj-art-b36ac595ed1c4dc2a88e6ef57ef53290 |
| institution | Kabale University |
| issn | 1319-1578 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-b36ac595ed1c4dc2a88e6ef57ef532902025-08-20T03:52:03ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782017-01-01291395310.1016/j.jksuci.2016.01.006Blind separation of multicomponent seismic wavefield using SVD of reduced dimension spectral matrixAbdullah Al-Hasanat0Abdelwadood Mesleh1Monther Krishan2Ahmed Sharadqh3Aws Al-Qaisi4W.L. Woo5S.S. Dlay6Dept. of Computer Engineering, Faculty of Engineering, University of Al-Hussien Bin Talal, JordanComputer Engineering Dept., Faculty of Engineering Technology, Al-Balqa’ Applied University, JordanMechatronics Engineering Dept., Faculty of Engineering Technology, Al-Balqa’ Applied University, JordanComputer Engineering Dept., Faculty of Engineering Technology, Al-Balqa’ Applied University, JordanCommunication Engineering Dept., Faculty of Engineering Technology, Al-Balqa’ Applied University, JordanSchool of Electrical, Electronic & Computer Engineering, Newcastle University, EnglandSchool of Electrical, Electronic & Computer Engineering, Newcastle University, EnglandThis 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%.http://www.sciencedirect.com/science/article/pii/S1319157816300180Blind separationMulticomponent seismic wavefieldSVD |
| spellingShingle | Abdullah Al-Hasanat Abdelwadood Mesleh Monther Krishan Ahmed Sharadqh Aws Al-Qaisi W.L. Woo S.S. Dlay Blind separation of multicomponent seismic wavefield using SVD of reduced dimension spectral matrix Journal of King Saud University: Computer and Information Sciences Blind separation Multicomponent seismic wavefield SVD |
| title | Blind separation of multicomponent seismic wavefield using SVD of reduced dimension spectral matrix |
| title_full | Blind separation of multicomponent seismic wavefield using SVD of reduced dimension spectral matrix |
| title_fullStr | Blind separation of multicomponent seismic wavefield using SVD of reduced dimension spectral matrix |
| title_full_unstemmed | Blind separation of multicomponent seismic wavefield using SVD of reduced dimension spectral matrix |
| title_short | Blind separation of multicomponent seismic wavefield using SVD of reduced dimension spectral matrix |
| title_sort | blind separation of multicomponent seismic wavefield using svd of reduced dimension spectral matrix |
| topic | Blind separation Multicomponent seismic wavefield SVD |
| url | http://www.sciencedirect.com/science/article/pii/S1319157816300180 |
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