Abstract：Since reflection coefficients obey the generalized Gaussian distribution in most cases, the Lp(0.7< p< 1.3) norm can be chosen as a measurement of sparsity. And the generalized Gaussian distribution keeps weak reflection information better than the modified Cauchy distribution when p< 1. Based on the assumption of the reflection-coefficient generalized Gaussian distribution, a non-convex Lp norm regularized blind deconvolution model is build according to the Bayesian sparse inversion theory. True solutions of reflection coefficients and wavelets can be simultaneously obtained with the alternative relaxation strategy. In order to get stable solutions, the non-convex Lp(0< p< 1) norm regularized problem is transformed to a re-weighted least-squares problem. Numerical tests show that the proposed approach can extract wavelets and reflection coefficients and efficiently improve the vertical resolution.
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