Nonstationary sparsity-constrained seismic deconvolution

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• 作者：Xue-Kai Sun (1) (2)
  Zandong Sun Sam (1) (2)
  Hui-Wen Xie (3)
  
  1. Lab for integration of Geology and Geophysics ; China University of Petroleum (Beijing) ; Beijing ; 102249 ; China
  2. State Key Lab for Petroleum Resources and Prospecting ; Beijing ; 102249 ; China
  3. Tarim Oilfield Company (CNPC) ; Korla ; 841000 ; China
  
• 关键词：nonstationarity ; sparsity constraint ; impedance constraint ; Gabor deconvolution ; log time ; frequency domain
• 刊名：Applied Geophysics
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：11
• 期：4
• 页码：459-467
• 全文大小：894 KB
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• 刊物主题：Geophysics/Geodesy; Geotechnical Engineering & Applied Earth Sciences;
• 出版者：Springer Berlin Heidelberg
• ISSN：1993-0658

文摘

The Robinson convolution model is mainly restricted by three inappropriate assumptions, i.e., statistically white reflectivity, minimum-phase wavelet, and stationarity. Modern reflectivity inversion methods (e.g., sparsity-constrained deconvolution) generally attempt to suppress the problems associated with the first two assumptions but often ignore that seismic traces are nonstationary signals, which undermines the basic assumption of unchanging wavelet in reflectivity inversion. Through tests on reflectivity series, we confirm the effects of nonstationarity on reflectivity estimation and the loss of significant information, especially in deep layers. To overcome the problems caused by nonstationarity, we propose a nonstationary convolutional model, and then use the attenuation curve in log spectra to detect and correct the influences of nonstationarity. We use Gabor deconvolution to handle nonstationarity and sparsity-constrained deconvolution to separating reflectivity and wavelet. The combination of the two deconvolution methods effectively handles nonstationarity and greatly reduces the problems associated with the unreasonable assumptions regarding reflectivity and wavelet. Using marine seismic data, we show that correcting nonstationarity helps recover subtle reflectivity information and enhances the characterization of details with respect to the geological record.