Filteration of multicomponent seismic wavefield data using frequency SVD

Al-Qaisi, Aws, Woo, Wai Lok and Dlay, Satnam (2009) Filteration of multicomponent seismic wavefield data using frequency SVD. In: EUSIPCO 2009 - 17th European Signal Processing Conference, 24th - 28th August 2009, Glasgow, UK.

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Abstract

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.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Covariance matrices, Vectors, Noise, Eigenvalues and eigenfunctions, Sensor arrays
Subjects:	H600 Electronic and Electrical Engineering
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Paul Burns
Date Deposited:	21 May 2019 11:17
Last Modified:	10 Oct 2019 18:47
URI:	http://nrl.northumbria.ac.uk/id/eprint/39371

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