基于变分模态分解和独立成分分析的矿山微震信号降噪
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摘要
微震信号降噪对P波、S波初至拾取、震源定位和震源机制反演等具有重要意义,为此提出一种基于变分模态分解和独立成分分析的微震信号降噪方法。采用变分模态分解将微震信号分解为特定数目的模态分量,再根据模态分量与原微震信号的相关系数剔除噪音特别大的模态分量。针对噪音与有用信号混合的模态分量,采用独立成分分析提取有用信号,再与剩余的低频模态分量相加得到VMD_ICA降噪信号。此外,采用正弦函数拟合的方法去除VMD_ICA降噪信号存在的工频噪音。信号测试表明:VMD_ICA法和VMD_ICA_Sine法降噪均能有效保留微震信号的局部特征,且其信噪比大于直接去除部分模态分量。矿山微震信号应用进一步表明:VMD_ICA法和VMD_ICA_Sine法均能提高微震信号的信噪比,有效地提高了PAI-K法P波初至拾取效果,且VMD_ICA_Sine法优于VMD_ICA法降噪效果。综上所述,VMD_ICA_Sine法能为矿山微震信号降噪提供一种较好的分析方法。
Microseismic signal denoising plays an important role in P and S phase arrival picking, seismic event location, focal mechanism inversion, and so on. To handle this problem, a variational mode decomposition(VMD) and independent component analysis(ICA) based method was proposed. Firstly, VMD was applied to decompose microseismic into certain number mode functions, then correlation coefficient between each mode function and original microseismic signal was used to remove mode functions which have a large noise. For noise and useful signal mixed mode functions, the ICA method was adopted to extract the useful signal, then the extracted useful signal was combined with the rest low frequency mode functions, which was called the VMD_ICA denoised signal. In addition, a Sine function was used to remove the power frequency noise which remained in the VMD_ICA denoised signal. A signal test shows that both the VMD_ICA method and the VMD_ICA_Sine method can retain microseismic signal local features effectively, and their signal to noise ratios(SNRs) are higher than that based on removing some mode functions directly. The mine microseismic signal application further indicates that the VMD_ICA method and the VMD_ICA_Sine method can improve microseismic signal's SNR and P phase arrival picking quality of the PAI-K method, and the VMD_ICA_Sine method has a better denoising performance than the VMD_ICA method. In conclusion, the VMD_ICA_Sine method provides a good way for mine microseismic signal denoising.
Microseismic signal denoising plays an important role in P and S phase arrival picking, seismic event location, focal mechanism inversion, and so on. To handle this problem, a variational mode decomposition(VMD) and independent component analysis(ICA) based method was proposed. Firstly, VMD was applied to decompose microseismic into certain number mode functions, then correlation coefficient between each mode function and original microseismic signal was used to remove mode functions which have a large noise. For noise and useful signal mixed mode functions, the ICA method was adopted to extract the useful signal, then the extracted useful signal was combined with the rest low frequency mode functions, which was called the VMD_ICA denoised signal. In addition, a Sine function was used to remove the power frequency noise which remained in the VMD_ICA denoised signal. A signal test shows that both the VMD_ICA method and the VMD_ICA_Sine method can retain microseismic signal local features effectively, and their signal to noise ratios(SNRs) are higher than that based on removing some mode functions directly. The mine microseismic signal application further indicates that the VMD_ICA method and the VMD_ICA_Sine method can improve microseismic signal's SNR and P phase arrival picking quality of the PAI-K method, and the VMD_ICA_Sine method has a better denoising performance than the VMD_ICA method. In conclusion, the VMD_ICA_Sine method provides a good way for mine microseismic signal denoising.
引文
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[10]梁喆,彭苏萍,郑晶.基于EMD和互信息熵的微震信号自适应去噪[J].计算机工程与应用,2014,50(4):7-11.LIANG Zhe, PENG Suping, ZHENG Jing. Self-adaptive denoising for microseismic signal based on EMD and mutual information entropy[J]. Computer Engineering and Applications,2014,50(4):7-11.
[11]秦晅,蔡建超,刘少勇,等.基于经验模态分解互信息熵与同步压缩变换的微地震信号去噪方法研究[J].石油物探,2017,56(5):658-666.QIN Xuan,CAI Jianchao,LIU Shaoyong,et al. Microseismic data denoising method based on EMD mutual information entropy and synchrosqueezing transform[J]. Geophysical Prospecting for Petroleum,2017,56(5):658-666.
[12] SHANG X Y,LI X B,MORALES-ESTEBAN A,et al.Enhancing micro-seismic P-phase arrival picking:EMD—Cosine function based denoising with application to the AIC picker[J]. Journal of Applied Geophysics,2018,150:325-337.
[13]梁喆,彭苏萍,郑晶. LEMD端点延拓方法及其在微震信号降噪中的应用[J].振动与冲击,2014,33(21):155-160.LIANG Zhe,PENG Suping,ZHENG Jing. LEMD endpoint extension method and the application in microseismic signal denoising[J]. Journal of Vibration and Shock,2014,33(21):155-160.
[14]HAN J,MIRKO V D B. Microseismic and seismic denoising via ensemble empirical mode decomposition and adaptive thresholding[J]. Geophysics,2015,80(6):69-80.
[15] KONSTANTIN D, DOMINIQUE Z. Variational mode decomposition[J]. IEEE Transactions Signal Processing,2014,62(3):531-544.
[16]张云强,张培林,王怀光,等.结合VMD和Volterra预测模型的轴承振动信号特征提取[J].振动与冲击,2018,37(3):129-135.ZHANG Yunqiang,ZHANG Peilin,WANG Huaiguang,et al. Feature extraction method for rolling bearing vibration signals based on VMD and Volterra prediction model[J].Journal of Vibration and Shock,2018,37(3):129-135.
[17] HYVARINEN A. Fast and robust fixed-point algorithms for independent component Analysis[J]. IEEE Transactions on Neural Networks,1999,10(3):626.
[18]KARAMZADEH N,DOLOEI G J,REZA A M. Automatic earthquake signal onset picking based on the continuous wavelet transform[J]. IEEE Transactions on Geoscience and Remote Sensing,2013,51(5):2666-2674.
[19]SARAGIOTIS C D,HADJILEONTIADIS L J,PANAS S M.PAI-S/K:a robust automatic seismic P phase arrival identification scheme[J]. Geoscience&Remote Sensing IEEE Transactions on,2002,40(6):1395-1404.
[20] LI X B,SHANG X Y,MORALES-ESTEBAN A,et al.Identifying P phase arrival of weak events:the Akaike information criterion picking application based on the empirical mode decomposition[J]. Computers&Geosciences,2017,100:57-66.
[2]姜福兴,尹永明,朱权洁,等.基于微震监测的千米深井厚煤层综放面支架围岩关系研究[J].采矿与安全工程学报,2014,31(2):167-174.JIANG Fuxing, YIN Yongming, ZHU Quanjie, et al.Relationship between support and surrounding rock of fully mechanized caving face in thick coal seam of kilometer deep mine based on microseismic monitoring technology[J].Journal of Mining&Safety Engineering,2014,31(2):167-174.
[3]尚雪义,李夕兵,彭康,等. FSWT_SVD模型在岩体微震信号特征提取中的应用[J].振动与冲击,2017,36(14):52-60.SHANG Xueyi,LI Xibing,PENG Kang,et al. Application of FSWT_SVD model for feature extraction of rock mass microseismic signal[J]. Journal of Vibration and Shock,2017,36(14):52-60.
[4]尚雪义,李夕兵,彭康,等.基于EMD_SVD的矿山微震与爆破信号特征提取及分类方法[J].岩土工程学报,2016,38(10):1849-1858.SHANG Xueyi, LI Xibing, PENG Kang, et al. Feature extraction and classification of mine microseism and blast based on EMD_SVD[J]. Chinese Journal of Geotechnical Engineering,2016,38(10):1849-1858.
[5]徐宏斌,李庶林,陈际经.基于小波变换的大尺度岩体结构微震监测信号去噪方法研究[J].地震学报,2012,34(1):85-96.XU Hongbin,LI Shulin,CHEN Jijing. A study on method of signal denoising based on wavelet transform for microseismicity monitoring in large-scale rockmass structures[J].Acta Seismologica Sinica,2012,34(1):85-96.
[6]李学龙,李忠辉,王恩元,等.矿山微震信号干扰特征及去噪方法研究[J].中国矿业大学学报,2015,44(5):788-792.LI Xuelong,LI Zhonghui,WANG Enyuan,et al. Study of mine microseismic signals interference characteristic and its de-noising method[J]. Journal of China University of Mining&Technology,2015,44(5):788-792.
[7]金晶晶,王旭,吴雪,等.基于改进阈值函数的体震信号平移不变去噪[J].东北大学学报(自然科学版),2009,30(3):333-336.JIN Jingjing, WANG Xu, WU Xue, et al. Translationinvariant de-noising of body fluttering signal based on improved threshold function[J]. Journal of Northeastern University(Natural Science),2009,30(3):333-336.
[8]曹思远,陈香朋.第二代小波变换及其在地震信号去噪中的应用[J].应用地球物理(英文版),2005,43(2):547-550.CAO Siyuan, CHEN Xiangpeng. The second-generation wavelet transform and its application in denoising of seismic data[J]. Applied Geophysics,2005,43(2):547-550.
[9]MOUSAVI S M,LANGSTON C A,HORTON S P. Automatic microseismic denoising and onset detection using the synchrosqueezed continuous wavelet transform[J].Geophysics,2016,81(4):341-355.
[10]梁喆,彭苏萍,郑晶.基于EMD和互信息熵的微震信号自适应去噪[J].计算机工程与应用,2014,50(4):7-11.LIANG Zhe, PENG Suping, ZHENG Jing. Self-adaptive denoising for microseismic signal based on EMD and mutual information entropy[J]. Computer Engineering and Applications,2014,50(4):7-11.
[11]秦晅,蔡建超,刘少勇,等.基于经验模态分解互信息熵与同步压缩变换的微地震信号去噪方法研究[J].石油物探,2017,56(5):658-666.QIN Xuan,CAI Jianchao,LIU Shaoyong,et al. Microseismic data denoising method based on EMD mutual information entropy and synchrosqueezing transform[J]. Geophysical Prospecting for Petroleum,2017,56(5):658-666.
[12] SHANG X Y,LI X B,MORALES-ESTEBAN A,et al.Enhancing micro-seismic P-phase arrival picking:EMD—Cosine function based denoising with application to the AIC picker[J]. Journal of Applied Geophysics,2018,150:325-337.
[13]梁喆,彭苏萍,郑晶. LEMD端点延拓方法及其在微震信号降噪中的应用[J].振动与冲击,2014,33(21):155-160.LIANG Zhe,PENG Suping,ZHENG Jing. LEMD endpoint extension method and the application in microseismic signal denoising[J]. Journal of Vibration and Shock,2014,33(21):155-160.
[14]HAN J,MIRKO V D B. Microseismic and seismic denoising via ensemble empirical mode decomposition and adaptive thresholding[J]. Geophysics,2015,80(6):69-80.
[15] KONSTANTIN D, DOMINIQUE Z. Variational mode decomposition[J]. IEEE Transactions Signal Processing,2014,62(3):531-544.
[16]张云强,张培林,王怀光,等.结合VMD和Volterra预测模型的轴承振动信号特征提取[J].振动与冲击,2018,37(3):129-135.ZHANG Yunqiang,ZHANG Peilin,WANG Huaiguang,et al. Feature extraction method for rolling bearing vibration signals based on VMD and Volterra prediction model[J].Journal of Vibration and Shock,2018,37(3):129-135.
[17] HYVARINEN A. Fast and robust fixed-point algorithms for independent component Analysis[J]. IEEE Transactions on Neural Networks,1999,10(3):626.
[18]KARAMZADEH N,DOLOEI G J,REZA A M. Automatic earthquake signal onset picking based on the continuous wavelet transform[J]. IEEE Transactions on Geoscience and Remote Sensing,2013,51(5):2666-2674.
[19]SARAGIOTIS C D,HADJILEONTIADIS L J,PANAS S M.PAI-S/K:a robust automatic seismic P phase arrival identification scheme[J]. Geoscience&Remote Sensing IEEE Transactions on,2002,40(6):1395-1404.
[20] LI X B,SHANG X Y,MORALES-ESTEBAN A,et al.Identifying P phase arrival of weak events:the Akaike information criterion picking application based on the empirical mode decomposition[J]. Computers&Geosciences,2017,100:57-66.
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