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Blind Deconvolution of Seismic Data Using f-Divergences
National Engineering Laboratory for Offshore Oil Exploration, Wave and Information Institute, School of Electronics and Information Engineering, Xi’an Jiao Tong University, No. 28 of West Xian’ning Road, Xi’an 710049, Shaanxi, China
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Received: 15 August 2011; in revised form: 10 September 2011 / Accepted: 14 September 2011 / Published: 19 September 2011
Abstract: This 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.
Keywords: blind deconvolution; f-divergence; mixture Jones’ family of distributions
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MDPI and ACS Style
Zhang, B.; Gao, J.-H. Blind Deconvolution of Seismic Data Using f-Divergences. Entropy 2011, 13, 1730-1745.
Zhang B, Gao J-H. Blind Deconvolution of Seismic Data Using f-Divergences. Entropy. 2011; 13(9):1730-1745.
Zhang, Bing; Gao, Jing-Huai. 2011. "Blind Deconvolution of Seismic Data Using f-Divergences." Entropy 13, no. 9: 1730-1745.