Seismic noise suppression using non-local means algorithm based on the Shearlet transform

WANG Jin-Gang; AN Yong; XU Zhen-Wang

doi:10.11720/wtyht.2023.2630

2023 Vol. 47, No. 1

Article Contents

Article navigation > Geophysical and Geochemical Exploration > 2023 > 47(1): 199-207

Next Article Previous Article

WANG Jin-Gang, AN Yong, XU Zhen-Wang. 2023. Seismic noise suppression using non-local means algorithm based on the Shearlet transform. Geophysical and Geochemical Exploration, 47(1): 199-207. doi: 10.11720/wtyht.2023.2630

Citation:

WANG Jin-Gang, AN Yong, XU Zhen-Wang. 2023. Seismic noise suppression using non-local means algorithm based on the Shearlet transform. Geophysical and Geochemical Exploration, 47(1): 199-207. doi: 10.11720/wtyht.2023.2630

Seismic noise suppression using non-local means algorithm based on the Shearlet transform

1. State Key Laboratory of Petroleum Resource and Prospecting,China University of Petroleum,Beijing 102249,China；
2. College of Geophysics,China University of Petroleum,Beijing 102200,China；
3. Research Institute of Petroleum Exploration and Development,Liaohe Oilfield Company,PetroChina,Panjin 124010,China

Received Date: 26 December 2021

Revised Date: 20 February 2023

Publish Date: 24 February 2023

Abstract

Abstract

Owing to the limitations of both the field environment for seismic data acquisition and the performance of instruments,the seismic signals collected in seismic exploration are inevitably mixed with strong noise,thus greatly affecting the subsequent processing and interpretation.In recent years,multi-scale geometric analysis methods have become an important topic in noise suppression owing to their unique advantages.This study proposed suppressing the seismic noise using a non-local mean (NLM) algorithm in the Shearlet domain.First,the non-subsampled Shearlet transform (NSST) was performed for seismic signals.Then,the decomposed coefficient subset was further processed using the NLM method,and the weight function was improved by using eight Sobel operators to approximate the omnidirectional structure.Finally,the inverse Shearlet transform was performed for the coefficients to obtain the denoised seismic signals.Experimental results show that this combined algorithm can effectively suppress the random noise and preserve the weak events,thus showing high practicability in the seismic data processing.
- random noise /
- Shearlet transform /
- Sobel operator /
- non-local mean /
- structure preservation

FullText(HTML)

References

[1]	李庆忠. 走向精确勘探的道路—高分辨率地震勘探系统工程剖析[M]. 北京: 石油工业出版社, 1993:1-196. Google Scholar
[2]	Li Q Z. The way to obtain a better resolution in seismic prospecting:A systematical analysis of high resolution seismic exploration[M]. Beijing: Petroleum Industry Press, 1993:1-196. Google Scholar
[3]	曹思远, 袁殿. 高分辨率地震资料处理技术综述[J]. 新疆石油地质, 2016, 37(1):112-119. Google Scholar
[4]	Cao S Y, Yuan D. A review of high-resolution seismic data processing approaches[J]. Xinjiang Petroleum Geology, 2016, 37(1):112-119. Google Scholar
[5]	刘洋, 李承楚. 地震资料信噪比估计的几种方法[J]. 石油地球物理勘探, 1997, 32(2):257-262. Google Scholar
[6]	Liu Y, Li C C. Some methods for estimating the signal /noise ratio of seismic data[J]. OGP, 1997, 32(2):257-262. Google Scholar
[7]	蔡斌. 非局部均值去噪算法研究[D]. 合肥: 中国科学技术大学, 2015. Google Scholar
[8]	Cai B. The research of non-local means denosing algorithm[D]. Hefei: University of Science and Technology of China, 2015. Google Scholar
[9]	王银杰. 基于非局部均值滤波的图像去噪算法[D]. 哈尔滨: 哈尔滨理工大学, 2019. Google Scholar
[10]	Wang Y J. The study of image denoising methods based on the non-local means[D]. Harbin: Harbin University of Science and Technology, 2019. Google Scholar
[11]	张丽果. 快速非局部均值滤波图像去噪[J]. 信号处理, 2013, 29(8):1043-1049. Google Scholar
[12]	Zhang L G. Fast non-local mean for image denoising[J]. Journal of Signal Processing, 2013, 29(8):1043-1049. Google Scholar
[13]	赵庆平, 陈得宝, 姜恩华, 等. 一种改进权重的非局部均值图像去噪算法[J]. 电子测量与仪器学报, 2014, 28(3):334-339. Google Scholar
[14]	Zhao Q P, Chen D B, Jiang E H, et al. Improved weighted non-local mean algorithm filter for image denoising[J]. Journal of Electronic Measurement and Instrumentation, 2014, 28(3):334-339. Google Scholar
[15]	Guo K, Labate D, Lim W Q. Wavelets with composite dilations[J]. Electronic Research Society, 2004, 10(9):78-87. Google Scholar
[16]	Han B, Kutyniok G, Shen Z. Adaptive multiresolution analysis structures and shearlet systems[J]. SIAM Journal on Numerical Analysis, 2011, 49:1921-1946. Google Scholar
[17]	Liang X Q, Li Y, Zhang C. Noise suppression for microseismic data by non-subsampled shearlet transform based on singular value decomposition[J]. Geophysical Prospecting, 2018, 66(5):894-903. Google Scholar
[18]	郭一民. 基于非下采样Shearlet变换的图像去噪算法研究[D]. 西安: 西安电子科技大学, 2013. Google Scholar
[19]	Guo Y M. Image noise reduction based on non-subsampled shearlet transfor[D]. Xi'an: Xidian University, 2013. Google Scholar
[20]	郭爱华, 路鹏飞, 余波, 等. 利用Shearlet变换提高叠后地震资料分辨率[J]. 石油地球物理勘探, 2021, 56(5):992-1000. Google Scholar
[21]	Guo A H, Lu P F, Yu B, et al. Improving post-stack seismic data resolution basedon Shearlet transform[J]. Oil Geophysical Prospecting, 2021, 56(5):992-1000. Google Scholar
[22]	程浩, 王德利, 王恩德, 等. 尺度自适应三维Shearlet变换地震随机噪声压制[J]. 石油地球物理勘探, 2019, 54 (5):970-978. Google Scholar
[23]	Cheng H, Wang D L, Wang E D, et al. Seismic random noise suppression based on scale adaptive 3D-Shearlet transform[J]. Oil Geophysical Prospecting, 2019, 54(5):970-978. Google Scholar
[24]	童思友, 高航, 刘锐, 等. 基于Shearlet变换的自适应地震资料随机噪声压制[J]. 石油地球物理勘探, 2019, 54(4):744-750. Google Scholar
[25]	Tong S Y, Gao H, Liu R, et al. Seismic random noise adaptive suppression based on the Shearlet transform[J]. Oil Geophysical Prospecting, 2019, 54(4):744-750. Google Scholar
[26]	蒋小忠. 基于联合双变量收缩Shearlet变换的微地震勘探噪声压制算法[D]. 长春: 吉林大学, 2018. Google Scholar
[27]	Jiang X Z. Denoising algorithm for microseismic exploration based on joint Bivariate Shrinkage in Shearlet Transform[D]. Changchun: Jilin University, 2018. Google Scholar
[28]	冯岩, 薛瑞. 剪切波理论及其应用研究进展[J]. 信阳师范学院学报:自然科学版, 2014, 27(3):463-468. Google Scholar
[29]	Feng Y, Xue R. Advances in theory and application of Shearlets[J]. Journal of Xinyang Normal University:Natural Science Edition, 2014, 27(3):463-468. Google Scholar
[30]	刘昕, 陈祖斌, 王东鹤, 等. 基于非下采样shearlet变换的微地震随机噪声压制[J]. 煤炭技术, 2016, 35(1):128-129. Google Scholar
[31]	Liu X, Chen Z B, Wang D H, et al. Microseismic random noise attenuation based on non-subsampled shearlet transform[J]. Coal Technology, 2016, 35(1):128-129. Google Scholar
[32]	王思涛, 金聪. 基于边缘提取的非局部均值图像去噪[J]. 电子测量技术, 2018, 41(11):99-102. Google Scholar
[33]	Wang S T, Jin C. Non-local mean image denoising based on edge extraction[J]. Electronic Measurement Technology, 2018, 41(11):99-102. Google Scholar
[34]	Souidene W, Beghdadi A, Abed-Meraim K. Image denoising in the transformed domain using non local neighborhoods[C]// New York:2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings,IEEE, 2006. Google Scholar
[35]	孙思亮, 刘怀山. 基于曲波变换和快速非局部均值的地震数据随机噪声压制[J]. 工程地球物理学报, 2021, 18(2):153-161. Google Scholar
[36]	Sun S L, Liu H S. Suppressing seismic random noise based on curvelet transform and fast non-local mean[J]. Chinese Journal of Engineering Geophysics, 2021, 18(2):153-161. Google Scholar
[37]	张小华, 张强. Shearlet域非局部均值图像去噪[J]. 新型工业化, 2011, 1(3):63-71. Google Scholar
[38]	Zhang X H, Zhang Q. Image denoising with non-local means in the shearlet domain[J]. The Journal of New Industrialization, 2011, 1(3):63-71. Google Scholar
[39]	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]. 重庆大学学报, 2021, 44(11):101-114. Google Scholar
[40]	Li M, Zhou Y T, Li M Y, et al. Denoising of seismic signals based on non-local mean in Shearlet domain[J] .Journal of Chongqing University, 2021, 44(11):101-114. Google Scholar
[41]	许志良, 邓承志. 基于非局部自相似的Shearlet自适应收缩图像去噪[J]. 计算机应用, 2015, 35(1):235-238. Google Scholar
[42]	Xu Z L, Deng C Z. Image denoising based on nonlocal self-similarity and Shearlet adaptive shrinkage model[J]. Journal of Computer Applications, 2015, 35(1):235-238. Google Scholar
[43]	Guo K H, Labate D. Optimally sparse multidimensional representation using shearlets[J]. SIAM Journal on Mathematical Analysis, 2007, 39(1):298-318. Google Scholar
[44]	Easley G, Labate D, Lim W. Sparse directional image representations using the discrete shearlet transform[J]. Applied and Computational Harmonic Analysis, 2008, 25:25-46. Google Scholar
[45]	Tasdizen T. Principal neighborhood dictionaries for nonlocal means image denoising[J]. IEEE Transactions on Image Processing, 2009, 18(12):2649-2660. Google Scholar
[46]	李岳松. 基于方向特征的图像去噪算法研究[D]. 西安: 西安理工大学, 2020. Google Scholar
[47]	Li Y S. Research on image denoising algorithm based on direction feature[D]. Xi'an: Xi'an University of Technology, 2020. Google Scholar