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Blind Deconvolution of Seismic Data Using f-Divergences
AbstractThis paper proposes a new approach to the seismic blind deconvolution problem in the case of band-limited seismic data characterized by low dominant frequency and short data records, based on Csiszár’s f-divergence. In order to model the probability density function of the deconvolved data, and obtain the closed form formula of Csiszár’s f-divergence, mixture Jones’ family of distributions (MJ) is introduced, by which a new criterion for blind deconvolution is constructed. By applying Neidell’s wavelet model to the inverse filter, we then make the optimization program for multivariate reduce to univariate case. Examples are provided showing the good performance of the method, even in low SNR situations.
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Zhang, B.; Gao, J.-H. Blind Deconvolution of Seismic Data Using f-Divergences. Entropy 2011, 13, 1730-1745.View more citation formats
Zhang B, Gao J-H. Blind Deconvolution of Seismic Data Using f-Divergences. Entropy. 2011; 13(9):1730-1745.Chicago/Turabian Style
Zhang, Bing; Gao, Jing-Huai. 2011. "Blind Deconvolution of Seismic Data Using f-Divergences." Entropy 13, no. 9: 1730-1745.