Journal of Jilin University(Earth Science Edition) ›› 2021, Vol. 51 ›› Issue (4): 1231-1242.doi: 10.13278/j.cnki.jjuese.20200112

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Random Noise Suppression of Seismic Data with Scale-Oriented Adaptive Threshold Based on Shearlet Transform

Chen Yijun, Cheng Hao, Gong Enpu, Xue Lin   

  1. Key Laboratory of Safe Mining of Deep Metal Mines(Northeastern University), Ministry of Education, Shenyang 110819, China
  • Received:2020-05-04 Online:2021-07-26 Published:2021-08-02
  • Supported by:
    Supported by the National Natural Science Foundation of China (41804103) and the National Key R&D Program of China (2017YFC1503101)

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Abstract: The effective signal of seismic prospecting is often interfered by random noise, which is difficult to identify. It is necessary to separate the random noise from the effective signal. The traditional Shearlet global threshold does not change with the direction and scale, which leads to the loss of many effective signals during denoising. As a new multi-scale and multi-directional time-frequency analysis method, Shearlet transform has the best sparse representation ability, local feature, and direction sensitivity. In this paper, the noisy seismic signal is decomposed by Shearlet to calculate the L2 norm of Shearlet domain coefficients in various scales and directions. After rearranging the data, it is found that the L2 norm decreases as the direction changes. Based on the L2 norm, a calculation method of adaptive threshold value of the scale direction of the number is proposed. Compared with wavelet transform, curvelet transform, and Shearlet global threshold denoising methods, the denoising results of simulated data and actual seismic records show that the method in this paper can remove the random noise and can also recover the weak deep signals well, and the signal noise ratio of the data is improved from 0.929 9 dB to 11.565 1 dB.

Key words: Shearle, seismic data, threshold improvement, signal noise ratio

CLC Number: 

  • P631.4
[1] 刘阳,赵虎,尹成,等. 起伏地表条件下的弹性波叠前逆时偏移[J]. 煤田地质与勘探,2019,47(1):181-186. Liu Yang, Zhao Hu, Yin Cheng, et al. Elastic Wave Prestack Reverse Time Migration on Undulating Surface[J]. Coal Geology & Exploration, 2019, 47(1):181-186.
[2] 张军华. 地震资料去噪方法:原理、算法、编程及应用[M]. 青岛:石油大学出版社,2011. Zhang Junhua. Seismic Data Denoising Method:Principle Algorithm Programming and Application[M]. Qingdao:China University of Petroleum Press, 2011.
[3] 万光南.f-k滤波在压制面波噪声中的应用[J]. 中州煤炭,2014(2):99-101. Wan Guangnan. Application off-k Filtering in Noise Suppression of Surface Wave[J]. Zhongzhou Coal, 2014(2):99-101.
[4] 贾春梅,姜国庆,刘志成,等. 频域稀疏双曲Radon变换去噪方法[J]. 物探与化探,2016,40(3):527-533. Jia Chunmei, Jiang Guoqing, Liu Zhicheng, et al. Denoising Method Based on Sparse Hyperbolic Radon Transform in the Frequency Domain[J]. Geophysical and Geochemical Exploration, 2016, 40(3):527-533.
[5] 曹善舒,李月,吴宁. 自适应径向道TFPF压制地震记录随机噪声[J]. 吉林大学学报(信息科学版),2016,34(2):10-17. Cao Shanshu,Li Yue,Wu Ning. Self-Adaptive Radial-Trace TFPF for Seismic Random Noise Attenuation[J]. Journal of Jilin University (Information Science Edition), 2016, 34(2):10-17.
[6] 张洁,李月. 平行径向道时频峰值滤波消减地震资料的随机噪声[J]. 吉林大学学报(工学版),2014,44(3):882-887. Zhang Jie, Li Yue. Reduction of Random Noise in Seismic Data by Parallel Radial-Trace Time-Frequency Peek Filtering[J]. Journal of Jilin University (Engineering and Technology Edition), 2014, 44(3):882-887.
[7] Li J, Meng K, Yuan L, et al. Adaptive Linear TFPF for Seismic Random Noise Attenuation[J]. Journal of Petroleum Exploration & Production Technology, 2018, 8(4):1-11.
[8] Zhuang G, Yue L, Liu Y, et al. Varying-Window-Length TFPF in High-Resolution Radon Domain for Seismic Random Noise Attenuation[J]. IEEE Geoscience & Remote Sensing Letters, 2014, 12(2):404-408.
[9] 刘霞,黄阳,黄敬,等. 基于经验模态分解(EMD)的小波熵阈值地震信号去噪[J]. 吉林大学学报(地球科学版), 2016, 46(1):262-269. Liu Xia, Huang Yang, Huang Jing, et al. Wavelet Entropy Threshold Seismic Signal Denoising Based on Empirical Mode Decomposition (EMD)[J]. Journal of Jilin University (Earth Science Edition), 2016, 46(1):262-269.
[10] 杨凯,刘伟. 基于改进EMD的地震信号去噪[J]. 西南石油大学学报,2012,34(4):75-82. Yang Kai, Liu Wei. Random Noise Attenuation of Seismic Signal Based on Improved EMD[J]. Journal of Southwest Petroleum University, 2012, 34(4):75-82.
[11] 孙哲,王建锋,王静,等. 基于时空变中值滤波的随机噪声压制方法[J]. 石油地球物理勘探,2016,51(6):1094-1102. Sun Zhe, Wang Jianfeng, Wang Jing, et al. Random Noise Elimination Based on the Time-Spacevariant Median Filtering[J]. Oil Geophysical Prospecting, 2016, 51(6):1094-1102.
[12] 王伟,高静怀,陈文超,等. 基于结构自适应中值滤波器的随机噪声衰减方法[J]. 地球物理学报,2012, 55(5):1732-1741. Wang Wei, Gao Jinghuai, Chen Wenchao, et al. Random Seismic Noise Suppression via Structure-Adaptive Median Filter[J]. Chinese Journal of Geophysics, 2012, 55(5):1732-1741.
[13] Kourouniotis F P, Kubichek R F, Boyd N G, et al. Application of the Wavelet Transform in Seismic Data Processing for the Development of New Noise Reduction Techniques[J]. Proc.SPIE-Wavelet Applications in Signal and Image Processing IV, 1996, 2825:620-631.
[14] Donoho D L. Wedgelets:Nearly Minimax Estimation of Edges[J]. Annals of Statistics, 1999, 27(3):859-897.
[15] Pennec E L, Mallat S. Sparse Geometric Image Representations with Bandelets[J]. IEEE Transactions on Image Processing, 2005, 14(4):423-438.
[16] Do M N, Vetterli M. The Finite Ridgelet Transform for Image Representation[J]. IEEE Transactions on Image Processing, 2003, 12(1):16-28.
[17] Starck J L, Candes E J, Donoho D L. The Curvelet Transform for Image Denoising[J]. IEEE Transactions on Image Processing, 2002, 11(6):670-684.
[18] 张华,陈小宏,李红星,等. 曲波变换三维地震数据去噪技术[J]. 石油地球物理勘探,2017,52(2):226-232. Zhang Hua, Chen Xiaohong, Li Hongxing, et al. 3D Seismic Data De-Noising Approach Based on Curve-Let Transform[J]. Oil Geophysical Prospecting, 2017, 52(2):226-232.
[19] Do M N, Vetterli M. The Contourlet Transform:An Efficient Directional Multiresolution Image Representation[J]. IEEE Transactions on Image Processing:A Publication of the IEEE Signal Processing Society, 2005, 14(12):2091-2106.
[20] 王建花,王守东,刘燕峰. 基于Contourlet系数相关性的地震噪声压制方法[J]. 中国海上油气,2016,28(1):39-44. Wang Jianhua, Wang Shoudong, Liu Yanfeng. Seismic Noise Suppression Method Based on Correlation of Contourlet Coefficients[J]. China Offshore Oil and Gas, 2016, 28(1):39-44.
[21] Easley G, Labate D, Lim W Q. Sparse Directional Image Representations Using the Discrete Shearlet Transform[J]. Applied & Computational Harmonic Analysis, 2008, 25(1):25-46.
[22] Guo K, Labate D, Lim W Q, et al. Wavelets with Composite Dilations and Their MRA Properties[J]. Applied & Computational Harmonic Analysis, 2006, 20(2):202-236.
[23] Labate D, Guo K. Optimally Sparse Multidimensional Representation Using Shearlets[J]. SIAM Journal on Mathematical Analysis, 2007, 39(1):298-318.
[24] Kong D, Peng Z. Seismic Random Noise Attenuation Using Shearlet and Total Generalized Variation[J]. Journal of Geophysics & Engineering, 2015, 12(6):1024-1035.
[25] Hosseini S A, Javaherian A, Hassani H, et al. Adaptive Attenuation of Aliased Ground Roll Using the Shearlet Transform[J]. Journal of Applied Geophysics, 2015, 112:190-205.
[26] 董新桐,马海涛,李月. 丘陵地带地震资料随机噪声压制新技术:高阶加权阈值函数的Shearlet变换[J]. 地球物理学报,2019,62(10):4039-4046. Dong Xintong, Ma Haitao, Li Yue. The New Technology for Suppression of Hilly Land Seismic Random Noise:Shearlet Transform and the High Order Weighted Threshold Function[J]. Chinese Journal of Geophysics, 2019, 62(10):4039-4046.
[27] 李民,周亚同,李梦瑶,等. Shearlet域基于非局部均值的地震信号去噪[J/OL]. 重庆大学学报.[2016-12-30]. http://kns.cnki.net/kcms/detail/50.1044.n.20191227.1804.011.html. Li Min, Zhou Yatong, Li Mengyao, et al. Denoising of Seismic Signals Based on Non-Local Mean in Shearlet Domain[J/OL]. Journal of Chongqing University.[2016-12-30].http://kns.cnki.net/kcms/detail/50.1044.n.20191227.1804.011.html.
[28] 李娟,计硕,李月,等. 基于Shearlet变换和峰度特性的井中微地震初至波拾取[J]. 吉林大学学报(工学版),2019,49(1):290-295. Li Juan, Ji Shuo, Li Yue, et al. First Arrival Pickup of Downhole Microseismic Signal Based on Shearlet Transform and Kurtosis Characteristic[J]. Journal of Jilin University (Engineering and Technology Edition), 2019, 49(1):290-295.
[29] 刘成明,王德利,胡斌,等. Shearlet域稀疏约束地震数据重建[J]. 吉林大学学报(地球科学版),2016,46(6):1855-1864. Liu Chengming, Wang Deli, Hu Bin, et al. Seismic Data Interpolation Based on Sparse Constraint in Shearlet Domain[J]. Journal of Jilin University (Earth Science Edition), 2016, 46(6):1855-1864.
[30] 郑升,李月,董新桐. Shearlet域深度残差CNN用于沙漠地震信号去噪[J]. 吉林大学学报(信息科学版), 2019,37(1):1-7. Zheng Sheng, Li Yue, Dong Xintong. Shearlet Domain Deep Residual CNN for Removing Noise from Desert Seismic Signals[J]. Journal of Jilin University (Information Science Edition), 2019, 37(1):1-7.
[1] Liu Yi, Liu Cai, Liu Yang, Gou Fuyan, Li Bingxiu. Adaptive Streaming Prediction Interpolation for Complex Seismic Wavefield [J]. Journal of Jilin University(Earth Science Edition), 2018, 48(4): 1260-1267.
[2] Wang Xiannan, Liu Sixin, Cheng Hao. Application of Shearlet Transform for Suppressing Random Noise in GPR Data [J]. Journal of Jilin University(Earth Science Edition), 2017, 47(6): 1855-1864.
[3] Liu Haiyan, Liu Cai, Wang Dian, Liu Yang. Seismic Data Discontinuity Identification Using Coherence Based on Facet Model Gradient Operator [J]. Journal of Jilin University(Earth Science Edition), 2017, 47(4): 1286-1294.
[4] Liu Chengming, Wang Deli, Hu Bin, Wang Tong. Seismic Data Interpolation Based on Sparse Constraint in Shearlet Domain [J]. Journal of Jilin University(Earth Science Edition), 2016, 46(6): 1855-1864.
[5] Xu Minghua, Li Rui, Lu Jiaotong, Meng Shan,Gong Xinglin. Study on the Recovery of Aliasing Seismic Data Based on the Compressive Sensing Theory [J]. Journal of Jilin University(Earth Science Edition), 2013, 43(1): 282-290.
[6] LI Hai-shan, WU Guo-chen, YI Xing-yao. Application of Morphological Component Analysis to Remove of Random Noise in Seismic Data [J]. J4, 2012, 42(2): 554-561.
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