%0 Conference Paper
%A Younis, M.S.
%A Hani, A.F.M.
%D 2007
%F scholars:143
%K Blind deconvolution; Convolutional noises; Eigenvector algorithms; Eigenvector approaches; Equalization techniques; Gaussian; Gaussian noises; Geological areas; Higher orders; In processing; Input signals; Minimum phase; Modified algorithms; Seismic datums, Acoustic intensity; Blind equalization; Earthquakes; Eigenvalues and eigenfunctions; Functions; Gaussian noise (electronic); Seismic response; Seismic waves; Seismology; Signal to noise ratio; Sulfate minerals; Trellis codes, Convolution
%P 1224-1229
%R 10.1109/ICIAS.2007.4658579
%T Modified HOS based Eigenvector algorithm for improvement of poor SNR of Seismic data
%U https://khub.utp.edu.my/scholars/143/
%X 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.
%Z cited By 0; Conference of 2007 International Conference on Intelligent and Advanced Systems, ICIAS 2007 ; Conference Date: 25 November 2007 Through 28 November 2007; Conference Code:74506