基于压缩感知的地震数据重建
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摘要
"两宽一高"(宽方位、宽频带、高密度)地震采集技术在解决复杂构造成像、岩性勘探以及油气藏精细描述等方面具有明显的技术优势,然而其工业化应用还需解决复杂地表以及勘探成本等制约因素。基于压缩感知理论及非规则观测系统的最优化设计,有效降低了勘探成本;随后利用L_0正则化和L_1正则化混合迭代的方法求解0范数稀疏约束的优化问题,重建地震数据,实现在一定成本限制的条件下获取更高密度数据体的目的。TFT工区基于压缩感知的数据采集与重建,验证了方法的有效性,为"两宽一高"地震勘探采集技术的工业化应用提供了有效的思路。
The"wide azimuth,wide band and high density"acquisition technology has distinct advantages in imaging complicated structures,lithology exploration and fine reservoir description.However,its industrial application still needs to solve the issues related to complex surfaces and exploration costs.Based on compressive sensing theory,the exploration cost can be effectively reduced through the design of non-uniform geometry.We propose a mixed iterative algorithm by using L_0 and L_1 regularization,with a zero-norm sparsity constraint,to solve the optimization problem and reconstruct seismic data.It could help to obtain high density data under a certain cost limit.Application in a TFT exploration region proved the effectiveness of the method,thus providing an effective industrial application of the"wide azimuth,wide band and high density"acquisition technology.
The"wide azimuth,wide band and high density"acquisition technology has distinct advantages in imaging complicated structures,lithology exploration and fine reservoir description.However,its industrial application still needs to solve the issues related to complex surfaces and exploration costs.Based on compressive sensing theory,the exploration cost can be effectively reduced through the design of non-uniform geometry.We propose a mixed iterative algorithm by using L_0 and L_1 regularization,with a zero-norm sparsity constraint,to solve the optimization problem and reconstruct seismic data.It could help to obtain high density data under a certain cost limit.Application in a TFT exploration region proved the effectiveness of the method,thus providing an effective industrial application of the"wide azimuth,wide band and high density"acquisition technology.
引文
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[16]孔丽云,于四伟,程琳,等.压缩感知技术在地震数据重建中的应用[J].地震学报,2012,34(5):659-666KONG L Y,YU S W,CHENG L,et al.Application of compressive sensing to the seismic data reconstruction[J].Acta Seismology Sinica,2012,34(5):659-666
[17]ZHANG H,CHEN X H,WU X M.Seismic data reconstruction based on CS and Fourier theory[J].Applied Geophysics,2013,10(2):170-180
[18]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
[2]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
[3]LI X,ARAVKINA Y,LEEUWEN VAN T,et al.Fast randomized full-waveform inversion with compressive sensing[J].Geophysics,2012,77(3):A13-A17
[4]MANSOUR H,HERRMANN F J,YILMAZ O.Improved wavefield reconstruction from randomized sampling via weighted one-norm minimization[J].Geophysics,2013,78,V193-V206
[5]HERRMANN F J,LI X.Efficient least-squares imaging with sparsity promotion and compressive sensing[J].Geophysical Prospecting,2012,60(4):696-712
[6]HERRMANN F J,WANG D,HENNENFENT G,et al.Curvelet-based seismic data processing:a multiscale and nonlinear approach[J].Geophysics,2008,73(1):A1-A5
[7]LENEMAN O.Random sampling of random processes:impulse response[J].Information and Control,1966,9(4):347-363
[8]DIPPE M,WOLD E.Stochastic sampling:theory and application[J].Progress in Computer Graphics,1992,1(1):1-54
[9]HENNENFENT G,HERRMANN F J.Simply denoise:wavefield reconstruction via jittered undersampling[J].Geophysics,2008,73(3):V19-V28
[10]MILTON A,TRICKETT S,Burroughs L.Reducing acquisition costs with random sampling and multidimensional interpolation[J].Expanded Abstracts of 81th Annual Internat SEG Mtg,2011:52-56
[11]MOSHER C C,KAPLAN S T,JANISZEWSKI F D.Non-uniform optimal sampling for seismic survey design[J].Expanded Abstracts of 74th EAGE Annual Conference,2012
[12]MOSHER C C,LI C,Morley L C,et al.Non-uniform optimal sampling for simultaneous source survey design[J].Expanded Abstracts of 84th Annual Internat SEG Mtg,2014:105-109
[13]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
[14]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
[15]LI C,MOSHER C C,KAPLAN S T.Interpolated compressive sensing for seismic data reconstruction[J].Expanded Abstracts of 82nd Annual Internat SEG Mtg,2012:1-6
[16]孔丽云,于四伟,程琳,等.压缩感知技术在地震数据重建中的应用[J].地震学报,2012,34(5):659-666KONG L Y,YU S W,CHENG L,et al.Application of compressive sensing to the seismic data reconstruction[J].Acta Seismology Sinica,2012,34(5):659-666
[17]ZHANG H,CHEN X H,WU X M.Seismic data reconstruction based on CS and Fourier theory[J].Applied Geophysics,2013,10(2):170-180
[18]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