Seismic random noise attenuation method based on the fast adaptive non-local means filtering algorithm

CUI Ya-Tong; WANG Sheng-Hou; CAI Zhong-Xian

doi:10.11720/wtyht.2022.1522

2022 Vol. 46, No. 5

Article Contents

Article navigation > Geophysical and Geochemical Exploration > 2022 > 46(5): 1187-1195

Next Article Previous Article

CUI Ya-Tong, WANG Sheng-Hou, CAI Zhong-Xian. 2022. Seismic random noise attenuation method based on the fast adaptive non-local means filtering algorithm. Geophysical and Geochemical Exploration, 46(5): 1187-1195. doi: 10.11720/wtyht.2022.1522

Citation:

CUI Ya-Tong, WANG Sheng-Hou, CAI Zhong-Xian. 2022. Seismic random noise attenuation method based on the fast adaptive non-local means filtering algorithm. Geophysical and Geochemical Exploration, 46(5): 1187-1195. doi: 10.11720/wtyht.2022.1522

Seismic random noise attenuation method based on the fast adaptive non-local means filtering algorithm

1. Tianjin Survey Design Institute Group Co.,Ltd.,Tianjin 300191,China；
2. School of Earth Resources,China University of Geosciences(Wuhan),Wuhan 430074,China

Received Date: 23 September 2021

Revised Date: 20 October 2022

Publish Date: 03 January 2023

Abstract

Abstract

The quality of seismic data plays a critical role in geological interpretation.However,the real seismic data usually contain a lot of noise,leading to fuzzy strata and unclear fault structures.The non-local means (NLM) filtering algorithm can effectively suppress random noise,but its computational efficiency is low.Therefore,it has limitations when being applied to large-scale seismic data processing.This study proposed a fast adaptive NLM algorithm,for which the computational efficiency was improved using the centrosymmetric data integration algorithm and the filtering parameters were adaptively adjusted using the standard deviation of similarity to estimate the homogeneity,thus further improving the noise attenuation effect.Therefore,the modified NLM filtering algorithm can effectively improve computational efficiency and enhance the noise attenuation effect.Furthermore,the feasibility and effectiveness of the algorithm were verified using model data and actual data.
- adaptive NLM filtering /
- centrosymmetric data integration /
- homogeneity estimation /
- random noise suppression

FullText(HTML)

References

[1]	Naghizadeh M. Seismic data interpolation and denoising in the frequency-wavenumber domain[J]. Geophysics, 2012, 77(2): V71-V80. Google Scholar
[2]	Latif A, Mousa W A. An efficient undersampled high-resolution radon transform for exploration seismic data processing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 55(2): 1010-1024. Google Scholar
[3]	鲁娥, 李庆春. 混合Radon变换地震噪声压制的应用[J]. 物探与化探, 2013, 37(4):706-710. Google Scholar
[4]	Lu E, Li Q C. The application of seismic noise attenuation based on hybrid radon transform[J]. Geophysical and Geochemical Exploration, 2013, 37(4): 706-710. Google Scholar
[5]	Oliveira M S, Henriques M V C, Leite F E A, et al. Seismic denoising using curvelet analysis[J]. Physica A: Statistical Mechanics and its Applications, 2012, 391(5): 2106-2110. Google Scholar
[6]	Yang H Y, Long Y, Lin J, et al. A seismic interpolation and denoising method with curvelet transform matching filter[J]. Acta Geophysica, 2017, 65: 1029-1042. Google Scholar
[7]	袁艳华, 王一博, 刘伊克, 等. 非二次幂Curvelet变换及其在地震噪声压制中的应用[J]. 地球物理学报, 2013, 56(3):1023-1032. Google Scholar
[8]	Yuan Y H, Wang Y B, Liu Y K, et al. Non-dyadic Curvelet transform and its application in seismic noise elimination[J]. Chinese Journal of Geophysics, 2013, 56(3): 1023-1032. Google Scholar
[9]	Huang W L, Wu R S, Wang R Q. Damped dreamlet representation for exploration seismic data interpolation and denoising[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(6), 3159-3172. Google Scholar
[10]	Goudarzi A, Riahi M A. Seismic coherent and random noise attenuation using the undecimated discrete wavelet transform method with WDGA technique[J]. Journal of Geophysics and Engineering, 2012, 9(6): 619-631. Google Scholar
[11]	Liu N H, Yang Y, Li Z, et al. Seismic signal de-noising using time-frequency peak filtering based on empirical wavelet transform[J]. Acta Geophysica, 2020, 68: 425-434. Google Scholar
[12]	Canales, Luis L. Random noise reduction[J]. SEG Technical Program Expanded Abstracts, 1984:329. Google Scholar
[13]	梁传坤. 频波谱在地震噪声分析与衰减中的应用[J]. 物探与化探, 1995, 19(1):34-40. Google Scholar
[14]	Liang C K. The application of frequency wave spectra to the analysis and attenuation of seismic noise[J]. Geophysical and Geochemical Exploration, 1995, 19(1): 34-40. Google Scholar
[15]	Liu Y, Liu N, Liu C. Adaptive prediction filtering in t-x-y domain for random noise attenuation using regularized nonstationary autoregression[J]. Geophysics, 2015, 80(1): V13-V21 Google Scholar
[16]	Bonar D, Sacchi M D. Denoising seismic data using the nonlocal means algorithm[J]. Geophysics, 2012, 77(1): A5-A8. Google Scholar
[17]	黄英, 文晓涛, 贺振华. 地震图像随机噪声的非局部均值去噪法[J]. 断块油气田, 2013, 20(6):730-732. Google Scholar
[18]	Huang Y, Wen X T, He Z H. Denoising algorithm of random noise with seismic image based on nonlocal means[J]. Fault-Block Oil & Gas Field, 2013, 20(6): 730-732. Google Scholar
[19]	Shang S, Han L G, Lyu Q T, et al. Seismic random noise suppression using an adaptive nonlocal means algorithm[J]. Applied Geophysics, 2013, 10(1): 33-40. Google Scholar
[20]	Manjón V J, Pierrick C, Luis M, et al. Adaptive non-local means denoising of mr images with spatially varying noise levels[J]. Journal of Magnetic Resonance Imaging, 2010, 31(1): 192-203. Google Scholar
[21]	Gao J J, Sacchi M D, Chen X H. A fast reduced-rank interpolation method for prestack seismic volumes that depend on four spatial dimensions[J]. Geophysics, 2013, 78(1): V21-V30. Google Scholar
[22]	Wang S H, Gao J J, Li J Y. A fast uncoiled randomized QR decomposition method for 5D seismic data reconstruction[J]. Journal of Seismic Exploration, 2018, 27(3): 255-276. Google Scholar
[23]	Xu Y K, Cao S Y, Pan X, et al. Random noise attenuation using a structure-oriented adaptive singular value decomposition[J]. Acta Geophysica, 2019, 67: 1091-106. Google Scholar
[24]	Gao J J, Stanton A, Sacchi M D. Parallel matrix factorization algorithm and its application to 5D seismic reconstruction and denoising[J]. Geophysics, 2015, 80(6): V173-V187. Google Scholar
[25]	Kreimer N, Sacchi M D. A tensor higher-order singular value decomposition for prestack seismic data noise reduction and interpolation[J]. Geophysics, 2012, 77(3): V113-V122. Google Scholar
[26]	Liu L, Plonka G, Ma J W. Seismic data interpolation and denoising by learning a tensor tight frame[J]. Inverse Problems, 2017, 33(10): 105011. Google Scholar
[27]	Si X, Yuan Y J, Si T H, et al. Attenuation of random noise using denoising convolutional neural networks[J]. Interpretation, 2019, 7(3): SE269-SE280. Google Scholar
[28]	Wang S N, Li Y, Zhao Y X. Desert seismic noise suppression based on multimodal residual convolutional neural network[J]. Acta Geophysica, 2020, 68: 389-401. Google Scholar
[29]	Zhao Y, Li Y, Dong X, et al. Low-frequency noise suppression method based on improved DnCNN in desert seismic data[J]. Geoscience and Remote Sensing Letters,IEEE, 2018, 16(5): 811-815. Google Scholar
[30]	郭奇, 曾昭发, 于晨霞, 等. 基于高精度字典学习算法的地震随机噪声压制[J]. 物探与化探, 2017, 41(5):907-913. Google Scholar
[31]	Guo Q, Zeng Z F, Yu C X, et al. Seismic random noise suppression based on the high-preicision dictionary learning algorithm[J]. Geophysical and Geochemical Exploration, 2017, 41(5): 907-913. Google Scholar
[32]	李勇, 张益明, 雷钦, 等. 模型约束下的在线字典学习地震弱信号去噪方法[J]. 地球物理学报, 2019, 62(1):411-420. Google Scholar
[33]	Li Y, Zhang Y M, Lei Q, et al. Online dictionary learning seismic weak signal denoising method under model constraints[J]. Chinese Journal of Geophysics, 2019, 62(1): 411-420. Google Scholar
[34]	Buades A, Coll B, Morel J M. Image denoising methods. A new nonlocal principle[J]. SIAM Review, 2010, 52(1): 113-147. Google Scholar
[35]	Coupe P, Yger, P, Prima S, et al. An optimized blockwise nonlocal means denoising filter for 3-d magnetic resonance images[J]. IEEE Transactions on Medical Imaging, 2008, 27(4): 425-441. Google Scholar
[36]	Lai R, Yang Y T. Accelerating non-local means algorithm with random project[J]. Electronics Letters, 2011, 47(3): 182-183. Google Scholar
[37]	周兵, 韩媛媛, 徐明亮, 等. 快速非局部均值图像去噪算法. 计算机辅助设计与图形学学报, 2016, 28(8):1260-1268. Google Scholar
[38]	Zhou B, Han Y Y, Xu M L. A fast non-local means image denoising algorithm[J]. Journal of Computer-Aided Design & Computer Graphics, 2016, 28(8): 1260-1268. Google Scholar
[39]	Maraschini M, Turton N. Random noise attenuation preserving geological detail - A fast and effective Non-Local-Means filter[C]// London: European Association of Geoscientists & Engineers, 2013. Google Scholar
[40]	Froment J. Parameter-free fast pixel wise non-local means denoising[J]. Image Processing on Line 4, 2014: 300-326. Google Scholar
[41]	Yang S, Chen A Q, Chen H D. Seismic data filtering using non-local means algorithm based on structure tensor[J]. Open Geosciences, 2017, 9(1): 151-160. Google Scholar
[42]	Zeng W L, Du Y J, Hu C H. Noise suppression by discontinuity indicator controlled non-local means method[J]. Multimedia Tools and Applications, 2017, 76: 13239-13253. Google Scholar
[43]	Chen P, Wu S Q, Fang H P, et al. Gaussian noise detection and adaptive non-local means filter[J]. China: Pacificrim Symposium on Image and Video Technology, 2017: 396-405. Google Scholar
[44]	Verma R, Pandey R. Grey relational analysis based adaptive smoothing parameter for non-local means image denoising[J]. Multimedia tools and applications, 2018, 77: 25919-25940. Google Scholar
[45]	Colom M, Buades A. Analysis and extension of the percentile method, estimating a noise curve from a single image[J]. Image Processing on Line 5, 2013, 365-390. Google Scholar
[46]	Wang J, Guo Y W, Ying Y T, et al. Fast non-local algorithm for image denoising[C]// Atlanta: IEEE International Conference on Image Processing, 2007:1429-1432. Google Scholar
[47]	Yu S, Sun J G, Meng X F, et al. Seismic random noise attenuation based on PCC classification in transform domain[J]. IEEE Access, 2019, 8:30368-30377. Google Scholar
[48]	Yusra A N, Chen S D. Comparison of image quality assessment: PSNR, HVS, SSIM, UIQI[J]. International Journal of scientific and Engineering Research, 2012, 3(8): 1-5. Google Scholar