A predictive deconvolution method for non-white-noise reflectivity*
Wang De-Ying, Kong Xue, Dong Lie-Qian, Chen Li-Hua, Wang Yong-Jun, and Wang Xiao-Chen
1. College of Earth Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China.
2. China University of Petroleum Shengli College, Dongying 257061, China.
3. BGP, CNPC, Zhuozhou 072751, China.
4. Network and Information Center, Shandong University of Science and Technology, Qingdao 266590, China.
Abstract 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 reflectivity series of field 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 filter 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 reflectivity 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.
This work was supported by Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents (No. 2017RCJJ034).
Corresponding Authors: Wang De-Ying (Email: wangdede@126.com)
E-mail: wangdede@126.com
About author: Wang De-Ying (Ph.D.) is a lecturer in the Department of Geophysics at the Shandong University of Science and Technology. He received his M.S. in Earth Exploration and Information Technology from China University of Petroleum (East China) in 2011. In 2014, he received his PH.D. in Geological Resources and Geological Engineering from China University of Petroleum (East China). From 2014 to 2017, he worked as a postdoctoral researcher at the Reservoir Geophysical Research Center of BGP, CNPC. His main interests are seismic data denoising and resolution enhancement.
Cite this article:
. A predictive deconvolution method for non-white-noise reflectivity*[J]. APPLIED GEOPHYSICS, 2019, 16(1): 109-123.
2019, Vol. 16 
