A predictive deconvolution method for non-white-noise refl ectivity

作     者：Wang De-Ying Kong Xue Dong Lie-Qian Chen Li-Hua Wang Yong-Jun Wang Xiao-Chen 

作者机构：College of Earth Science and EngineeringShandong University of Science and TechnologyQingdao 266590China China University of Petroleum Shengli CollegeDongying 257061China BGPCNPCZhuozhou 072751China Network and Information CenterShandong University of Science and TechnologyQingdao 266590China 

出 版 物：《Applied Geophysics》 (应用地球物理（英文版）)

年 卷 期：2019年第16卷第1期

页      面：101-115页

核心收录：

学科分类：07[理学] 0707[理学-海洋科学] 0708[理学-地球物理学] 0825[工学-航空宇航科学与技术] 0704[理学-天文学] 

基　　金：supported by Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents(No.2017RCJJ034) 

主　　题：Non-white reflectivity series predictive deconvolution spectrum-modeling multiples resolution autocorrelation 

摘      要：Conventional predictive deconvolution assumes that the reflection coefficients of the earth conform to an uncorrelated white noise sequence. The Wiener-Hopf (WH) equation is constructed to solve the filter and eliminate the correlated components of the seismic records, attenuate multiples, and improve seismic resolution. However, in practice, the primary refl ectivity series of fi eld data rarely satisfy the white noise sequence assumption, with the result that the correlated components of the primary reflectivity series are also eliminated by traditional deconvolution. This results in signal distortion. To solve this problem, we have proposed an improved method for deconvolution. First, we estimated the wavelet correlation from seismic records using the spectrum-modeling method. Second, this wavelet autocorrelation was used to construct a new autocorrelation function which contains the correlated components caused by the existence of multiples and avoids the correlated components of the primary reflectivity series. Finally, the new autocorrelation function was brought into the WH equation, and the predictive fi lter operator was calculated for deconvolution. In this paper, we have applied this new method to simulated and field data processing, and we have compared its performance with that of traditional predictive deconvolution. Our results show that the new method can adapt to non-white refl ectivity series without changing the statistical characteristics of the primary reflection coefficient series. Compared with traditional predictive deconvolution, the new method reduces processing noise and improves fidelity, all while maintaining the ability to attenuate multiples and enhance seismic resolution.