Filteration of multicomponent seismic wavefield data using frequency SVD

Aws Al-Qaisi*, W. L. Woo, S. S. Dlay

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


This paper proposes a new statistical approach based on frequency singular value decomposition (SVD) to enhance the SNR of the noisy multicomponent seismic wavefield. Our filtering algorithm consists of three main steps: Firstly, the frequency transformed multicomponent seismic wavefield data is rearranged into one long vector containing information on all frequencies and all component interactions. Secondly, the reduced dimensional spectral covariance matrix of the long vector data is estimated by means of singular value decomposition. Finally, the separation of the primary seismic waves from the noise is achieved by projecting the dominant eigenvector that has the highest eigenvalue of the reduced dimensional covariance matrix onto the long data vector. The experimental results have shown that the proposed algorithm outperforms the conventional separation technique in terms of accuracy and complexity.

Original languageEnglish
Title of host publication2009 17th European Signal Processing Conference
Number of pages5
ISBN (Print)9781617388767
Publication statusPublished - 1 Dec 2009
Event17th European Signal Processing Conference, EUSIPCO 2009 - Glasgow, United Kingdom
Duration: 24 Aug 200928 Aug 2009

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491


Conference17th European Signal Processing Conference, EUSIPCO 2009
Country/TerritoryUnited Kingdom


Dive into the research topics of 'Filteration of multicomponent seismic wavefield data using frequency SVD'. Together they form a unique fingerprint.

Cite this