3D Block Matching Seismic Data Denoising Based on Curvelet Noise Estimation

SUN Chengyu1,2 DIAO Juncai1,2 LI Wenjing3

(1.School of Geosciences, China University of Petroleum (East China), Qingdao, Shandong Province, China 266580)
(2.Function Laboratory of Marine Geo-Resource Evaluation and Exploration Technology, Laboratory for Marine Mineral Resources, Qingdao, Shandong Province, China 266071)
(3.Research & Development Center, BGP Inc., CNPC, Zhuozhou, Hebei Province, China 072751)

【Abstract】Conventional block matching 3D (BM3D) algorithms are used for seismic data denoising. However, some parameters such as filtering threshold are difficult to be determined because of the lack of prior noise information in the practical processing. In this paper, an improved BM3D denoising method based on the curvelet noise estimation is developed for seismic data. First the noise variance of seismic data is estimated by the curvelet transform. Then appropriate threshold parameters are adaptively determined. Finally the noise elimination is accurately achieved by this improved BM3D. Based on model and real data tests, the proposed algorithm can better eliminate random noise and protect signals than the conventional BM3D and curvelet transform. Furthermore, the proposed algorithm maintains most detailed information of boundary reflection and its computational efficiency is relatively high.

【Keywords】 seismic data denoising; block matching 3D (BM3D); noise prior estimation; curvelet transform; signal-to-noise ratio (SNR);


【Funds】 National Natural Science Foundation of China (41874153) National Science and Technology Major Project (2016ZX05006-002-003)

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    [1] WANG Huazhong, FENG Bo, WANG Xiongwen, et al. Compressed sensing and its application in seismic exploration [J]. Geophysical Prospecting for Petroleum, 2016, 55 (4): 467–474 (in Chinese).

    [2] LI Zhenchun, ZHANG Junhua. The Method of Seismic Data Processing [M]. China University of Petroleum Press, Dongyong, Shangdong, 2004 (in Chinese).

    [3] 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 (in Chinese).

    [4] Wei Z, Duan C, Jiang S, et al. The improved Winner filter image restoration based on partition [C]. IEEE 6th International Symposium on Computational Intelligence and Design, 2014, 198–200.

    [5] LI Haishan, WU Guochen, YIN Xingyao. Application of morphological component analysis to remove of random noise in seismic data [J]. Journal of Jilin University (Earth Science Edition), 2012, 42 (2): 554–561 (in Chinese).

    [6] XUE Zhao, DONG Liangguo, SHAN Lianyu. Amplitude preservation theoretical analysis of Radon transforms de-noising method [J]. Oil Geophysical Prospecting, 2012, 47 (6): 858–867 (in Chinese).

    [7] Mairal J, Sapiro G, Elad M. Learning multiscale sparse representations for image and video restoration [J]. SIAM Multiscale Modeling and Simulation, 2008, 7 (1): 214–241.

    [8] Buades A, Coll J, Morel M. A non-local algorithm for image denoising [C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2005, 60–65.

    [9] Kostadin D, Alessandro F, Vladimir K, et al. Image denoising by sparse 3-D transform-domain collaborative filtering [J]. IEEE Transactions on Image Processing, 2007, 16 (8): 2080–2095.

    [10] XU Zhongwei. The Non-local and Block Matching 3-D Filtering Algorithm [D]. Xidian University, Xi’an, Shaanxi, 2011 (in Chinese).

    [11] Wang Z, Bovik A C, Sheikh H R, et al. Image quality assessment: From error visibility to structural similarity [J]. IEEE Transactions on Image Processing, 2004, 13 (4): 600–612.

    [12] Amani S, Gholami A, Niestanak A J. Seismic random noise attenuation via 3D block matching [J]. Journal of Applied Geophysics, 2017, 136: 353–363 (in Chinese).

    [13] HAN Yulan, XUAN Shibin, LIU Xiangpin. Image denoising with a fast block-matching and 3D filtering algorithm [J]. Journal of Guangxi University for Nationalities (Natural Science Edition), 2015, 21 (2): 73–80 (in Chinese).

    [14] LI Wenjing, SUN Chengyu, HAO Ge, et al. Application of improved 3D block matching algorithm in seismic random noise attenuation [C]. Annual Meeting of Chinese Geosciences Union, 2017 (in Chinese).

    [15] HUANG Mu, HUANG Wenqing, LI Junbai, et al. Parameters study based on BM3D image denoising algorithm [J]. Industrial Control Computer, 2014, 27 (10): 99–101 (in Chinese).

    [16] WANG Yan, LI Xiaoyan, MU Xiuqing, et al. A new adaptive denoising method based on BM3D for catenary image [J]. Journal of The China Railway Society, 2016, 38 (4): 59–65 (in Chinese).

    [17] Candès E and Donoho D. Continuous curvelet transform [J]. Applied and Computational Harmonic Ana-lysis, 2005, 19 (2): 198–222.

    [18] ZHANG Henglei, ZHANG Yuncui, SONG Shuang, et al. Curvelet domain-based prestack seismic data denoise method [J]. Oil Geophysical Prospecting, 2008, 43 (5): 508–513 (in Chinese).

    [19] XUE Shigui. The curvelet transform for seismic random de-noising using cycle spinning method [J]. Progress in Geophysics, 30 (1): 372–377 (in Chinese).

    [20] YAO Zhen’an, SUN Chengyu, SHI Xiaolei, et al. A combined denoising method based on Curvelet transform and anisotropic diffusion filtering [J]. Acta Petrolei Sinica, 2016, 37 (4): 490–498 (in Chinese).

    [21] YANG Hui, ZHANG Hua, WANG Dongnian, et al. Random noise attenuation of seismic data based on curvelet transform and EMD [J]. Chinese Journal of Engineering Geophysics, 2018, 15 (1): 79–85 (in Chinese).

    [22] CAO Jingjie, YANG Zhiquan, YANG Yong, et al. An adaptive seismic random noise elimination method based on Curvelet transform [J]. Geophysical Prospecting for Petroleum, 2018, 57 (1): 72–78, 121 (in Chinese).

    [23] BAO Qianzong, CHEN Wenchao, GAO Jinghuai. Seismic data random noise attenuation based on the second generation Curvelet transform [J]. Coal Geology & Exploration, 2010, 38 (1): 66–70 (in Chinese).

    [24] ZHANG Hua, CHEN Xiaohong, LI Hongxing, et al. 3D seismic data de-noising approach based on Curvelet transform [J]. Oil Geophysical Prospecting, 2017, 52 (2): 226–232 (in Chinese).

    [25] LIU Lei, LIU Zhen, ZHANG Junhua. Denoising and detecting seismic weak signal based on curvelet thresholding method [J]. Progress in Geophysics, 2011, 26 (4): 1415–1422 (in Chinese).

    [26] YU Jiangqi, CAO Siyuan, CHEN Hongling, et al. Sparse deconvolution based on Curvelet transform of improved threshold [J]. Oil Geophysical Prospecting, 2017, 52 (3): 426–433 (in Chinese).

This Article


CN: 13-1095/TE

Vol 54, No. 06, Pages 1188-1194+1171

December 2019


Article Outline


  • 0 Introduction
  • 1 Principles
  • 2 Model tests
  • 3 Application example
  • 4 Conclusion
  • References