基于双重稀疏表示的地震资料随机噪声衰减方法

doi:10.11720/wtyht.2018.1297

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Abstract

The double sparse dictionary is adopted for the seismic random noise attenuation.The seismic data are not represented well by the fixed dictionaries,which do not contain the effective information about the seismic data;the learning dictionaries are fully adaptable but are costly to deploy in the big data processing.The double sparse dictionary reduces the number of training sample and is more suitable for the construction of high-dimension dictionary and the analysis of the high-dimension signal. With the over completed discrete cosine transform as the base dictionary,the sparse dictionary is trained by the sparse K-SVD driven by the noisy seismic data samples.Thus the seismic random noise attenuation model based on the double sparse dictionary is established.A comparison of the results of the synthesized and real data in high dimensional case shows that the seismic random noise can be suppressed effectively by the method based on double sparse dictionary and the fault structure can be preserved in 3D case.

Key words： noise attenuation sparse representation learning dictionary morphological component analysis sparse K-SVD

Received: 29 June 2017 Published: 04 June 2018

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	Articles by authors
	Yong LUO
	Hai-Bo MAO
	Xiao-Hai YANG
	Wen-Jie LI
	Wen-Chao CHEN

Cite this article:

Yong LUO,Hai-Bo MAO,Xiao-Hai YANG, et al. Seismic random seismic noise attenuation method on basis of the double sparse representation[J]. Geophysical and Geochemical Exploration, 2018, 42(3): 608-615.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2018.1297 OR https://www.wutanyuhuatan.com/EN/Y2018/V42/I3/608

[1]	胡志荣, 何政泉 . 从世界与中国油气供需形势看中国油气安全[J]. 长春师范学院学报, 2013,( 3):8-12.
[2]	李干生 . 中国石化油气勘探十年回顾与展望[J]. 当代石油石化, 2009,( 3):1-8.
[3]	Chopra S, Marfurt K J . Seismic attributes:a historical perspective[J]. Geophysics, 2005, 70(5):3SO-28SO.
[4]	Chopra S, Marfurt K J . Emerging and future trends in seismic attributes[J]. The Leading Edge, 2008,27(3):298-318.
[5]	Chopra S, Marfurt K J . Coherence and curvature attributes on preconditioned seismic data[J]. The Leading Edge, 2011,30(4):386-393.
[6]	国九英, 周兴元 . 用 f-x域预测技术消除随机噪音[J]. 石油地球物理勘探, 1992,27(5):655-661.
[7]	Harris P E, White R E . Improving the performance of f-x prediction filtering at low signal-to-noise ratios[J]. Geophysical Prospecting, 1997,45:269-302.
[8]	Jones I F, Levy S . Signal-to-noise ratio enhancement in multichannel seismic data via the Karhunen-Loeve transform[J]. Geophysical Prospecting, 1987,35(1):12-32.
[9]	刘保童, 朱光明 . 高精度广义KL变换波场分离与去噪[J]. 地球科学与环境学报, 2005,27(3):59-62.
[10]	Bekara M, van der Baan M . Local singular value decomposition for signal enhancement of seismic data[J]. Geophysics, 2007,72(2):V59-V65.
[11]	Goupillaud P, Grossmann A, Morlet J . Cycle-Octave and related transforms in seismic signal analysis[J]. Geoexploration, 1984,23(1):85-102.
[12]	Candes E J . Ridgelets:theory and applications[D]. Stanford:Stanford University, 1998.
[13]	Candes E J, Donoho D L . New tight frames of curvelets and optimal representations of objects with C2 singularities[J]. Communications on Pure and Applied Mathematics, 2004,57:219-266.
[14]	Candes E J, Demanet L, Donoho D L , et al. Fast discrete curvelet transforms[J]. Multiscale modeling and Simulation, 2005,5:861-899.
[15]	Hennenfent G, Herrmann F J . Simply denoise:Wavefield reconstruction via jittered undersampling[J]. Geophysics, 2008,73(3):V19-V28.
[16]	Neelamani R, Baumstein A, Gillard D . Coherent and random noise attenuation using the curvelet transform[J]. The Leading Edge, 2008,27(2):240-248.
[17]	Zheng J J, Yin X Y, Zhang G Z , et al. The surface wave suppression using the second generation curvelet transform[J]. Applied Geophysics, 2010,7(4):325-335.
[18]	Hennenfent G, Fenelon L, Herrmann F J . Nonequispaced curvelet transform for seismic data reconstruction:A sparsity-promoting approach[J]. Geophysics, 2010, 75 (6):WB203-WB210.
[19]	郑静静, 印兴耀, 张广智 . 基于Curvelet变换的多尺度分析技术[J]. 石油地球物理勘探, 2009,44(5):543-547.
[20]	Olshausen B A . Emergence of simple-cell receptive field properties by learning a sparse code for natural images[J]. Nature, 1996,381(6583):607-609.
[21]	Donoho D L, Elad M . Optimally sparse representation in general (nonorthogonal) dictionaries via l1 minimization[J]. Proceedings of the National Academy of Sciences, 2003,100(5):2197-2202.
[22]	Rubinstein R, Zibulevsky M, Elad M . Double sparsity: Learning sparse dictionaries for sparse signal approximation[J]. IEEE Transactions on Signal Processing, 2010,58(3):1553-1564.