Modified HOS based Eigenvector algorithm for improvement of poor SNR of Seismic data
In this paper, a modified blind deconvolution Eigenvector approach based on higher order of statistic has been proposed. The given technique is to process the seismogram with poor SNR, dominant by the convolution noise. Seismogram is the output of a mixed phase source wavelet driven by the non Gauss...
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| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Published: |
2007
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| Online Access: | http://eprints.utp.edu.my/481/1/paper.pdf http://www.scopus.com/inward/record.url?eid=2-s2.0-57949116731&partnerID=40&md5=fa2f6131228e1fdf9a3f242de7180413 http://eprints.utp.edu.my/481/ |
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| Institution: | Universiti Teknologi Petronas |
| Summary: | In this paper, a modified blind deconvolution Eigenvector approach based on higher order of statistic has been proposed. The given technique is to process the seismogram with poor SNR, dominant by the convolution noise. Seismogram is the output of a mixed phase source wavelet driven by the non Gaussian input signal in presence of additive Gaussian, color Gaussian noise. Existing HOS based techniques are good in processing of non-minimum phase system but most of them fails when noise dominates the actual signal. In regions like volcanic, anhydrite, complex geological areas, it is difficult to acquire the seismic data with good SNR, and convolution noise is dominant. Convolutional noise makes it difficult to identify the closely spaced bedding. Proposed blind equalization technique is based on the eigenvector algorithm [17], certain modifications are incorporated to reduce the MMSE, max. Distortion and convolution noise effectively. Performance of the modified algorithm indicates its effectiveness for signals with dominant Gaussian/color Gaussian noise. ©2007 IEEE.
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