CEEMDAN自适应阈值去噪算法在地震方向的应用
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摘要
提出一种基于自适应完备集合经验模态分解(CEEMDAN,complete ensemble empirical mode decomposition with adaptive noise)的自适应阈值去噪算法。含噪信号经CEEMDAN算法分解成若干个模态分量(IMF,intrinsic mode functions),根据样本熵理论,对IMF分量中高频分量自适应选取,根据噪声和有用信息与原始信号的相关性不同,对高频分量中的噪声系数定位,利用能量熵选取噪声主区间,用高频分量中噪声主区间的噪声系数方差作为阈值,对高频分量进行阈值去噪,进一步去除噪声,保留高频中的有用信息,最后将信噪分离的高频分量和低频分量重构。分别对合成和实际地震信号去噪处理,并与常规去噪算法进行对比。数据仿真和实验结果表明,在原始信号信噪比为0.5dB时,常规与改进算法去噪后信噪比分别为4.55dB和9.97dB,大幅提高信噪比,达到随机噪声压制的目的,实现了高频分量的自适应选取和高频分量中有用信息的再提取。
An adaptive threshold denoising algorithm based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)is proposed in this paper.The noisy signal is decomposed into several modal components(IMF)by CEEMDAN algorithm.Based on the sample entropy theory,the adaptive selection of high frequency components in IMF components is realized,and the noise figure in high frequency components is located according to the different correlation between noise and useful information or the original signal.The main noise interval is selected by energy entropy,and the noise coefficient variance of the main noise interval in high frequency components is used as the threshold.Threshold denoising of high-frequency components is carried out to further remove noise and retain useful information in high-frequency.Finally,high-frequency components and low-frequency components separated from signal-noise are reconstructed.The denoising of synthetic and actual seismic signals is processed separately and compared with conventional denoising algorithms.Data simulation and experimental results show that when the signal-to-noise ratio of the original signal is 0.5 dB,the signalto-noise ratio obtained by the conventional and improved algorithms is 4.55 dB and 9.97 dB respectively,which indicates significant improvement in the signal-to-noise ratio,achieving the purpose of random noise suppression and realizing the self-adaptive selection of high-frequency components and the re-extraction of useful information from high-frequency components.
An adaptive threshold denoising algorithm based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)is proposed in this paper.The noisy signal is decomposed into several modal components(IMF)by CEEMDAN algorithm.Based on the sample entropy theory,the adaptive selection of high frequency components in IMF components is realized,and the noise figure in high frequency components is located according to the different correlation between noise and useful information or the original signal.The main noise interval is selected by energy entropy,and the noise coefficient variance of the main noise interval in high frequency components is used as the threshold.Threshold denoising of high-frequency components is carried out to further remove noise and retain useful information in high-frequency.Finally,high-frequency components and low-frequency components separated from signal-noise are reconstructed.The denoising of synthetic and actual seismic signals is processed separately and compared with conventional denoising algorithms.Data simulation and experimental results show that when the signal-to-noise ratio of the original signal is 0.5 dB,the signalto-noise ratio obtained by the conventional and improved algorithms is 4.55 dB and 9.97 dB respectively,which indicates significant improvement in the signal-to-noise ratio,achieving the purpose of random noise suppression and realizing the self-adaptive selection of high-frequency components and the re-extraction of useful information from high-frequency components.
引文
[1]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.(in Chinese)
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[3]Li H G,Hu Y,Li F C,et al.Succinct and fast empirical mode decomposition[J].Mechanical Systems and Signal Processing,2017,85:879-895.
[4]Dragomiretskiy K,Zosso D.Variational mode decomposition[J].IEEE Transactionson Signal Processing,2014,62(3):531-544.
[5]王姣,李振春,王德营,等.基于CEEMD的地震数据小波阈值去噪方法研究[J].石油物探,2014,53(2):164-172.WANG Jiao,LI Zhenchun,WANG Deying,et al.A method for wavelet threshold denoising of seismic data based on CEEMD[J].Geophysical Prospecting for Petroleum,2014,53(2):164-172.(in Chinese)
[6]李文,刘霞,段玉波,等.基于小波熵和相关性的高分辨率阈值去噪方法[J].数据采集与处理,2013,28(3):371-375.LI Wen,LIU Xia,DUAN Yubo,et al.High-resolutiont threshold denoising method based on wavelet entropy and correlation[J].Journal of Data Acquisition&Processing,2013,28(3):371-375.(in Chinese)
[7]Rosso O A,Blanco S,Yordanova J,et al.Wavelet entropy:anew tool for analysis of short duration brain electrical signals[J].Journal of Neuroscience Methods,2001,105(1):65-75.
[8]Wu Q M,Wang J,Peng X Y,et al.Fault diagnosis of rotating machinery using Gaussian process and EEMD-treelet[J].International Journal of Adaptive Control and Signal Processing,2019,33(1):52-73.
[9]Li Y X,Li Y A,Chen X,et al.A new underwater acoustic signal denoising technique based on CEEMDAN,mutual information,permutation entropy,and wavelet threshold denoising[J].Entropy,2018,20(8):563.
[10]Bouhalais M L,Djebala A,Ouelaa N,et al.CEEMDAN and OWMRA as a hybrid method for rolling bearing fault diagnosis under variable speed[J].The International Journal of Advanced Manufacturing Technology,2018,94(5/6/7/8):2475-2489.
[11]张杏莉,卢新明,贾瑞生,等.基于变分模态分解及能量熵的微震信号降噪方法[J].煤炭学报,2018,43(2):356-363.ZHANG Xingli,LU Xinming,JIA Ruisheng,et al.Micro-seismic signal denoising method based on variational mode decomposition and energy entropy[J].Journal of China Coal Society,2018,43(2):356-363.(in Chinese)
[12]Zhou Z B,Lin L,Li S X.International stock market contagion:A CEEMDAN wavelet analysis[J].Economic Modelling,2018,72:333-352.[LinkOut]
[13]李锋,林阳阳,赵辉,等.基于CEEMDAN-SVM的液压泵故障诊断方法研究[J].液压与气动,2016(1):125-129.LI Feng,LIN Yangyang,ZHAO Hui,et al.Fault diagnosis of hydraulic pump based on CEEMDAN-SVM[J].Chinese Hydraulics&Pneumatics,2016(1):125-129.(in Chinese)
[14]刘颜明.基于变分模态分解的滚动轴承故障诊断系统研究[D].大庆:东北石油大学,2018.LIU Yanming.Research on bearing fault diagnosis system based on variational mode decomposition[D].Daqing:Northeast Petroleum University,2018.(in Chinese)
[15]黄阳.基于经验模态分解的轴承故障诊断系统研究[D].大庆:东北石油大学,2016.HUANG Yang.Research on bearing fault diagnosis system based on empirical mode decomposition[D].Daqing:Northeast Petroleum University,2016.(in Chinese)
[16]Wang Y H.The ricker wavelet and the lambert w function[J].Geophysical Journal International,2015,200(1):111-115.
[2]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(自然科学版),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(Seience&Technology Edition),2012,34(4):75-82.(in Chinese)
[3]Li H G,Hu Y,Li F C,et al.Succinct and fast empirical mode decomposition[J].Mechanical Systems and Signal Processing,2017,85:879-895.
[4]Dragomiretskiy K,Zosso D.Variational mode decomposition[J].IEEE Transactionson Signal Processing,2014,62(3):531-544.
[5]王姣,李振春,王德营,等.基于CEEMD的地震数据小波阈值去噪方法研究[J].石油物探,2014,53(2):164-172.WANG Jiao,LI Zhenchun,WANG Deying,et al.A method for wavelet threshold denoising of seismic data based on CEEMD[J].Geophysical Prospecting for Petroleum,2014,53(2):164-172.(in Chinese)
[6]李文,刘霞,段玉波,等.基于小波熵和相关性的高分辨率阈值去噪方法[J].数据采集与处理,2013,28(3):371-375.LI Wen,LIU Xia,DUAN Yubo,et al.High-resolutiont threshold denoising method based on wavelet entropy and correlation[J].Journal of Data Acquisition&Processing,2013,28(3):371-375.(in Chinese)
[7]Rosso O A,Blanco S,Yordanova J,et al.Wavelet entropy:anew tool for analysis of short duration brain electrical signals[J].Journal of Neuroscience Methods,2001,105(1):65-75.
[8]Wu Q M,Wang J,Peng X Y,et al.Fault diagnosis of rotating machinery using Gaussian process and EEMD-treelet[J].International Journal of Adaptive Control and Signal Processing,2019,33(1):52-73.
[9]Li Y X,Li Y A,Chen X,et al.A new underwater acoustic signal denoising technique based on CEEMDAN,mutual information,permutation entropy,and wavelet threshold denoising[J].Entropy,2018,20(8):563.
[10]Bouhalais M L,Djebala A,Ouelaa N,et al.CEEMDAN and OWMRA as a hybrid method for rolling bearing fault diagnosis under variable speed[J].The International Journal of Advanced Manufacturing Technology,2018,94(5/6/7/8):2475-2489.
[11]张杏莉,卢新明,贾瑞生,等.基于变分模态分解及能量熵的微震信号降噪方法[J].煤炭学报,2018,43(2):356-363.ZHANG Xingli,LU Xinming,JIA Ruisheng,et al.Micro-seismic signal denoising method based on variational mode decomposition and energy entropy[J].Journal of China Coal Society,2018,43(2):356-363.(in Chinese)
[12]Zhou Z B,Lin L,Li S X.International stock market contagion:A CEEMDAN wavelet analysis[J].Economic Modelling,2018,72:333-352.[LinkOut]
[13]李锋,林阳阳,赵辉,等.基于CEEMDAN-SVM的液压泵故障诊断方法研究[J].液压与气动,2016(1):125-129.LI Feng,LIN Yangyang,ZHAO Hui,et al.Fault diagnosis of hydraulic pump based on CEEMDAN-SVM[J].Chinese Hydraulics&Pneumatics,2016(1):125-129.(in Chinese)
[14]刘颜明.基于变分模态分解的滚动轴承故障诊断系统研究[D].大庆:东北石油大学,2018.LIU Yanming.Research on bearing fault diagnosis system based on variational mode decomposition[D].Daqing:Northeast Petroleum University,2018.(in Chinese)
[15]黄阳.基于经验模态分解的轴承故障诊断系统研究[D].大庆:东北石油大学,2016.HUANG Yang.Research on bearing fault diagnosis system based on empirical mode decomposition[D].Daqing:Northeast Petroleum University,2016.(in Chinese)
[16]Wang Y H.The ricker wavelet and the lambert w function[J].Geophysical Journal International,2015,200(1):111-115.
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