基于压缩感知的地震数据采集实践
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摘要
介绍了基于压缩感知的非规则观测系统设计以及在中国西部沙漠地区实现的第一块基于压缩感知的地震数据采集。在进行压缩感知观测系统设计时采用贪心序贯算法,以逐点增加的方式确定检波点与炮点的位置并构建观测矩阵,按照观测矩阵与稀疏变换矩阵的不相关性来确定观测系统,然后利用确定的观测系统,基于探区的速度模型进行正演模拟,以验证观测系统的有效性,从而确定最终的观测系统。针对中国西部沙漠试验区的具体情况设计了块状非规则(随机)采样观测系统,纵横向检波点在一定约束条件下不均匀分布、激发点近于随机不规则分布。首次采用可控震源进行了数据采集,获得了1760炮的地震数据。通过数据重建,得到了4倍密度(7.5m×7.5m的面元)数据体,偏移成像后的地震剖面品质比常规剖面(15m×15m)明显提高。与规则高密度采集相比,此次基于压缩感知的地震数据采集虽然检波点、炮点大幅度减少,但重建后的高密度规则数据的偏移成像质量有了明显提高。该稀疏采集试验不仅为后续高密度数据重建研究提供了宝贵的实际资料和应用经验,而且对于东部复杂障碍区的地震数据采集也具有借鉴意义。采用"节点仪器+压缩感知+可控震源"的采集方式,将是一种最佳组合,会取得更高的效益和更好的效果。
The designing of an irregular geometry based on compressive sensing and its first application in the desert region of western China are discussed.The geometric design based on compressive sensing uses the greedy sequential algorithm to determine the position of the receiver point and shot point and construct an observation matrix via the point-by-point increased method.Then,the geometry is predetermined according to the irrelevance of the observation and the sparse transformation matrices.Thereafter,this predetermined geometry is used to perform forward modeling based on the velocity model to examine and confirm the final geometry in the working area.A blocky irregular(random)geometry was experimentally designed in the desert area of western China.The receiver points were unevenly distributed in two directions(in-line and cross-line)with certain constraints,the shot points were randomly distributed,and vibroseis was used to obtain 1760 shot records in total.Through data reconstruction,a data cube was obtained which had a density(7.5 m ×7.5 mbin)four times the original data(15 m ×15 m),and the seismic migration profile was significantly improved compared with the conventional profile.Compared with the regular high-density acquisition,the number of receiver and shot points in compressive sensing based geometry,is greatly reduced,with significant improvement in the quality of the migration profile.This sparse acquisition experiment provides valuable practical information and experience for future high-density data reconstruction and is also very useful for seismic data acquisition in the eastern complex exploration area in China.The"Node instrument+ Compressive Sensing + Vibroseis"approach is recommended to achieve better acquisition results with higher efficiencies.
The designing of an irregular geometry based on compressive sensing and its first application in the desert region of western China are discussed.The geometric design based on compressive sensing uses the greedy sequential algorithm to determine the position of the receiver point and shot point and construct an observation matrix via the point-by-point increased method.Then,the geometry is predetermined according to the irrelevance of the observation and the sparse transformation matrices.Thereafter,this predetermined geometry is used to perform forward modeling based on the velocity model to examine and confirm the final geometry in the working area.A blocky irregular(random)geometry was experimentally designed in the desert area of western China.The receiver points were unevenly distributed in two directions(in-line and cross-line)with certain constraints,the shot points were randomly distributed,and vibroseis was used to obtain 1760 shot records in total.Through data reconstruction,a data cube was obtained which had a density(7.5 m ×7.5 mbin)four times the original data(15 m ×15 m),and the seismic migration profile was significantly improved compared with the conventional profile.Compared with the regular high-density acquisition,the number of receiver and shot points in compressive sensing based geometry,is greatly reduced,with significant improvement in the quality of the migration profile.This sparse acquisition experiment provides valuable practical information and experience for future high-density data reconstruction and is also very useful for seismic data acquisition in the eastern complex exploration area in China.The"Node instrument+ Compressive Sensing + Vibroseis"approach is recommended to achieve better acquisition results with higher efficiencies.
引文
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[2] CANDES E J,WAKIN M B.An introduction to compressive sampling:A sensing/sampling paradigm that goes against the common knowledge in data acquisition[J].IEEE Signal Processing Magazine,2008,25(2):21-30
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[12]王升超,韩立国,巩向博.基于各向异性Radon变换的叠前地震数据重建[J].石油物探,2016,55(6):808-815WANG S C,HAN L G,GONG X B.Prestack seismic data reconstruction by anisotropic Radon transform[J].Geophysical Prospecting for Petroleum,2016,55(6):808-815
[13]刘争光,韩立国,张良,等.压缩感知理论下基于快速不动点连续算法的地震数据重建[J].石油物探,2018,57(1):50-57LIU Z G,HAN L G,ZHANG L,et al.Seismic data reconstruction using FFPC algorithm based on compressive sensing[J].Geophysical Prospecting for Petroleum,2018,57(1):50-57
[14]屠宁.基于压缩感知的快速最小二乘逆时偏移[J].石油物探,2018,57(1):86-93TU N.Fast least-squares reverse-time migration via compressive sensing[J].Geophysical Prospecting for Petroleum,2018,57(1):86-93
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[17] MOLDOVEANU N.Random sampling:a new strategy for marine acquisition[J].Expanded Abstracts of 80th SEG Annual Internat Mtg,2010:51-55
[18] HENNENFENT G,HERRMANN F J.Simply denoise:wavefield reconstruction via jittered undersampling[J].Geophysics,2008,73(3):V19-V28
[19] MOSHER C C,KAPLAN S T,JANISZEWSKI F D.Non-uniform optimal sampling for seismic survey design[J].Expanded Abstracts of EAGE Annual Conference,2012:77-84
[20] LEO B,MOSHER C C,LI C B,et al.Application of compressive seismic imaging at Lookout Field,Alaska[J].Expanded Abstracts of EAGE Annual Conference,2017:670-676
[21]王华忠,冯波,王雄文,等.压缩感知及其在地震勘探中的应用[J].石油物探,2016,55(4):467-474WANG H Z,FENG B,WANG X W,et al.Compressed sensing and its application in seismic exploration[J].Geophysical Prospecting for Petroleum,2016,55(4):467-474
[22]崔永福,郭念民,吴国忱,等.不规则观测系统数据规则化及其在相干噪声压制中的应用[J].石油物探,2016,55(4):524-532CUI Y F,GUO N M,WU G C,et al.Regularization of irregular geometry seismic data and its application in the coherent noise suppression[J].Geophysical Prospecting for Petroleum,2016,55(4):524-532
[23]陈生昌,陈国新,王汉闯.稀疏性约束的地球物理数据高效采集方法初步研究[J].石油物探,2015,54(1):24-35CHEN S C,CHEN G X,WANG H C.The preliminary study on high efficient acquisition of geophysical data with sparsity constraints[J].Geophysical Prospecting for Petroleum,2015,54(1):24-35
[24] LIN T T Y,HERRMANN F J.Designing simultaneous acquisitions with compressive sensing[J].Expanded Abstracts of 71st EAGE Annual Conference,2009:4-9
[25]马坚伟.压缩感知走进地球物理勘探[J].石油物探,2018,57(1):24-27MA J W.Compressive sensing in geophysical exploration[J].Geophysical Prospecting for Petroleum,2018,57(1):24-27
[26]周松,吕尧,吕公河,等.基于压缩感知的非规则地震勘探观测系统设计与数据重建[J].石油物探,2017,56(5):617-625ZHOU S,LV Y,LV G H,et al.Irregular seismic geometry design and data reconstruction based on compressive sensing[J].Geophysical Prospecting for Petroleum,2017,56(5):617-625
[27]舒国旭,吕公河,吕尧,等.基于压缩感知的地震数据重建[J].石油物探,2018,57(4):549-554SHU G X,LV G H,LV Y,et al.Seismic data reconstruction based on compressive sensing[J].Geophysical Prospecting for Petroleum,2018,57(4):549-554
[2] CANDES E J,WAKIN M B.An introduction to compressive sampling:A sensing/sampling paradigm that goes against the common knowledge in data acquisition[J].IEEE Signal Processing Magazine,2008,25(2):21-30
[3] HERRMANN F J.Randomized sampling and sparsity:Getting more information from fewer samples[J].Geophysics,2010,75(6):WB173-WB187
[4] HERRMANN F J.WANG D L,Hennenfent G,et al.Curvelet-based seismic data processing:A multiscale and nonlinear approach[J].Geophysics,2008,73(1):A1-A5
[5]白兰淑,刘伊克,卢回忆,等.基于压缩感知的Curvelet域联合迭代地震数据重建[J].地球物理学报,2014,57(9):2937-2945BAI L S,LIU Y K,LU H Y,et al.Curvelet-domain joint iterative seismic data reconstruction based on compressed sensing[J].Chinese Journal of Geophysics,2014,57(9):2937-2945
[6] CANDES E J,ROMBERG J,TAO T.Robust uncertainty principles:exact signal reconstruction from highly incomplete frequency information[J].IEEE Transactions on Information Theory,2006,52(2):489-509
[7] WU R,WU B,GENG Y.Imaging in compressed domain using dreamlets[R].Beijing 2009International Geophysical Conference and Exposition,2009:212
[8] NAGHIZADEH M,SACCHI M D.On sampling functions and Fourier reconstruction methods[J].Geophysics,2010,75(6):WB137-WB151
[9] LI C B,CHALES C,MOSHER et al.Interpolatated compressive sensing for seismic data reconstruction[J].Expanded Abstracts of 82nd Annual Internat SEG Mtg,2012:6-12
[10]张良,韩立国,刘争光,等.基于压缩感知和Contourlet变换的地震数据重建方法[J].石油物探,2017,56(6):804-811ZHANG L,HAN L G,LIU Z G,et al.Seismic data reconstruction based on compressed sensing and Contourlet transform[J].Geophysical Prospecting for Petroleum,2017,56(6):804-811
[11]冯飞,王征,刘成明,等.基于Shearlet变换稀疏约束地震数据重建[J].石油物探,2016,55(5):682-691FENG F,WANG Z,LIU C M,et al.Seismic data reconstruction based on sparse constraint in the Shearlet domain[J].Geophysical Prospecting for Petroleum,2016,55(5):682-691
[12]王升超,韩立国,巩向博.基于各向异性Radon变换的叠前地震数据重建[J].石油物探,2016,55(6):808-815WANG S C,HAN L G,GONG X B.Prestack seismic data reconstruction by anisotropic Radon transform[J].Geophysical Prospecting for Petroleum,2016,55(6):808-815
[13]刘争光,韩立国,张良,等.压缩感知理论下基于快速不动点连续算法的地震数据重建[J].石油物探,2018,57(1):50-57LIU Z G,HAN L G,ZHANG L,et al.Seismic data reconstruction using FFPC algorithm based on compressive sensing[J].Geophysical Prospecting for Petroleum,2018,57(1):50-57
[14]屠宁.基于压缩感知的快速最小二乘逆时偏移[J].石油物探,2018,57(1):86-93TU N.Fast least-squares reverse-time migration via compressive sensing[J].Geophysical Prospecting for Petroleum,2018,57(1):86-93
[15] MOSHER C C,KESKULA E,KAPLAN S T,et al.Compressive seismic imaging[J].Expanded Abstracts of 82nd SEG Annual Internat Mtg,2012:1-5
[16]周亚同,刘志峰,张志伟.形态分量分析框架下基于DCT与曲波字典组合的地震信号重建[J].石油物探,2015,54(5):560-568,581ZHOU Y T,LIU Z F,ZHANG Z W.Seismic signal reconstruction under the morphological component analysis framework combined with DCT and Curvelet dictionary[J].Geophysical Prospecting for Petroleum,2015,54(5):560-568,581
[17] MOLDOVEANU N.Random sampling:a new strategy for marine acquisition[J].Expanded Abstracts of 80th SEG Annual Internat Mtg,2010:51-55
[18] HENNENFENT G,HERRMANN F J.Simply denoise:wavefield reconstruction via jittered undersampling[J].Geophysics,2008,73(3):V19-V28
[19] MOSHER C C,KAPLAN S T,JANISZEWSKI F D.Non-uniform optimal sampling for seismic survey design[J].Expanded Abstracts of EAGE Annual Conference,2012:77-84
[20] LEO B,MOSHER C C,LI C B,et al.Application of compressive seismic imaging at Lookout Field,Alaska[J].Expanded Abstracts of EAGE Annual Conference,2017:670-676
[21]王华忠,冯波,王雄文,等.压缩感知及其在地震勘探中的应用[J].石油物探,2016,55(4):467-474WANG H Z,FENG B,WANG X W,et al.Compressed sensing and its application in seismic exploration[J].Geophysical Prospecting for Petroleum,2016,55(4):467-474
[22]崔永福,郭念民,吴国忱,等.不规则观测系统数据规则化及其在相干噪声压制中的应用[J].石油物探,2016,55(4):524-532CUI Y F,GUO N M,WU G C,et al.Regularization of irregular geometry seismic data and its application in the coherent noise suppression[J].Geophysical Prospecting for Petroleum,2016,55(4):524-532
[23]陈生昌,陈国新,王汉闯.稀疏性约束的地球物理数据高效采集方法初步研究[J].石油物探,2015,54(1):24-35CHEN S C,CHEN G X,WANG H C.The preliminary study on high efficient acquisition of geophysical data with sparsity constraints[J].Geophysical Prospecting for Petroleum,2015,54(1):24-35
[24] LIN T T Y,HERRMANN F J.Designing simultaneous acquisitions with compressive sensing[J].Expanded Abstracts of 71st EAGE Annual Conference,2009:4-9
[25]马坚伟.压缩感知走进地球物理勘探[J].石油物探,2018,57(1):24-27MA J W.Compressive sensing in geophysical exploration[J].Geophysical Prospecting for Petroleum,2018,57(1):24-27
[26]周松,吕尧,吕公河,等.基于压缩感知的非规则地震勘探观测系统设计与数据重建[J].石油物探,2017,56(5):617-625ZHOU S,LV Y,LV G H,et al.Irregular seismic geometry design and data reconstruction based on compressive sensing[J].Geophysical Prospecting for Petroleum,2017,56(5):617-625
[27]舒国旭,吕公河,吕尧,等.基于压缩感知的地震数据重建[J].石油物探,2018,57(4):549-554SHU G X,LV G H,LV Y,et al.Seismic data reconstruction based on compressive sensing[J].Geophysical Prospecting for Petroleum,2018,57(4):549-554