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《Chinese Journal of Geophysics》 2015-02
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Estimating primaries by sparse inversion of passive-source seismic data with L1-norm constraint

CHENG Hao;WANG De-Li;FENG Fei;WANG Tong;College of Geo-Exploration Science and Technology,Jilin University;Data Processing Center of Geophysics,China Oilfield Services Limited;  
This work has improved the original algorithm to estimate primaries by sparse inversion of passive-source seismic data through replacing the original algorithm to solve the convex optimization problem with L1-norm constraint.It avoids using a time-window to prevent the inversion from into local optimization situations when estimating primaries by sparse inversion.Moreover,during the solving of the optimization problem with L1-norm constraint,2DCurvelet transform and wavelet transform are used at the same time.In 2D Curvelet and wavelet domains,the data become more sparse,then the results obtained are more accurate and the quality of imaging is improved.First,the method of convex optimization problem with L1-norm constraint is introduced to solve the problem of estimating primaries by sparse inversion of passive-source seismic data,instead of the steepest descent method under L0-norm constraint.Second,2DCurvelet transformand wavelet transform are combined during the sparse inversion.In the 2D Curvelet-wavelet domain,the data become more sparse.Comparing with 3DCurvelet transform,the velocity of 2D Curvelet-wavelet transform is improved.Third,a simple model and a complex model are used to simulate the passive seismic data.The method of convex optimization problem with L1-norm constraint and that combined with 2D Curvelet transform and wavelet transform are used to estimate primaries from the passive seismic data,respectively.At last,comparison with the results obtained by the traditional LSQR algorithm illustrates that the method proposed is feasible and effective.The method of estimating primaries by sparse inversion can directly estimate primaries from the passive seismic data,and obtain the virtual-shot gathers which are free of the surface-related multiples.Under the assumption that the data is sparse,this work uses the method of convex optimization problem with L1-norm constraint to replace the traditional one to estimate primaries,which avoids using a time-window to prevent the inversion from into local optimization situations during sparse inversion,and improves the precision of the primaries estimated.It also suppresses artificial influence and improves the imaging quality.Comparing with the result obtained by lsqr algorithm shows the accuracy and superiority of the convex optimization problem with L1-norm constraint.During sparse inversion by L1-norm inversion,2DCurvelet transform and wavelet transform are combined to make the data more sparse,and improve the precision of primaries estimated.At the same time,the artificial influence suppression is improved further.Comparing with the traditional method to estimate primaries,the convex optimization problem with L1-norm constraint can avoid using a time-window to prevent the inversion from into local optimization situations,and the primaries estimated become more accurate.2DCurvelet transform and wavelet transform introduced make the data sparse,and improve the precision of primaries estimated.
【Fund】: 国家自然科学基金项目(41374108);; 国家科技重大专项(2011ZX05023-005-008)资助
【CateGory Index】: P631.44
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【Citations】
Chinese Journal Full-text Database 2 Hits
1 ZHU Heng1,WANG De-li1*,SHI Zhi-an2,FENG Fei1(1.College of Geo-Exploration Science and Technology of Jilin University,Changchun 130026,China; 2.Coal Geological and Geophysical Prospecting Company of Jilin Province,Changchun 130033,China);Passive seismic imaging of seismic interferometry[J];Progress in Geophysics;2012-02
2 Feng Fei 1,Wang De-Li 1,Zhu Heng 1,and Cheng Hao 11.College of Geo-Exploration Science and Technology,Jilin University,Changchun 130026,China.;Estimating primaries by sparse inversion of the 3D Curvelet transform and the L1-norm constraint[J];应用地球物理(英文版);2013-02
【Co-citations】
Chinese Journal Full-text Database 2 Hits
1 Wang Tong;Wang Deli;Feng Fei;Cheng Hao;Sun Hailong;College of GeoExploration Science and Technology,Jilin University;;3D Surface-Related Multiple Elimination[J];Journal of Jilin University(Earth Science Edition);2014-06
2 Meng Gege;Wang Deli;Chen Xin;College of Geo-Exploration Science and Technology,Jilin University;;Study on seismic data denoising method based on 3D Curvelet transform[J];Geophysical Prospecting for Petroleum;2014-03
【Secondary Citations】
Chinese Journal Full-text Database 10 Hits
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