基于SS-CWT和Yoon-V自适应阈值的弱振幅微震分频降噪方法
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摘要
多发于采空区的弱振幅微震信号具有强度弱、不平稳、噪源强的特点,基于同步挤压小波变换(SS-CWT)及Yoon-V自适应阈值提出分频降噪方法.首先,SS-CWT重构含噪信号,位于高能噪声的频段使用软阈值降噪;其次,为保留高低频逆转换的有效信息,选用合适的参数进行Yoon-V自适应阈值降噪.该方法相比传统小波变换具有分辨率高的优势,相较经验模态分析(EMD)而言更能提取信号中的弱振幅分量.最后用SS-CWT时频谱对比分析降噪前后信号特征,并用复合变异系数定权T值法对本次降噪效果进行评价,本文方法很好的保留有效信息,降噪效果显著.
Weak amplitude micro-seismic received around mined out area has characterized by weak of intensity,non-stationary and strong noise of source,we provide the method of de-noising by divided frequency for wake amplitude based on SS-CWT and customed threshold Yoon-V. Above all,noise-contained original signal is reconstructed by Synchro-Squeezed Continuous Wavelet Transform(SS-CWT),of the high-energy noise de-noising by hard threshold. Then, useful information is remained during reverse conversion from high-low frequency,reasonable parameter selected to de-noise by costumed threshold Yoon-V. Compared with the traditional wavelet transform,this method has the advantage of high resolution,and it can extract the weak amplitude component of the signal more effectively than the Empirical Mode Decomposition(EMD). In the end,the characteristics of the signal before and after noise reduction are analyzed by SS-CWT time spectrum comparison,and the noise reduction effect is evaluated by the composite variable coefficient weighting T-value method. Keep effective information method in this paper is valid,obvious effect.
Weak amplitude micro-seismic received around mined out area has characterized by weak of intensity,non-stationary and strong noise of source,we provide the method of de-noising by divided frequency for wake amplitude based on SS-CWT and customed threshold Yoon-V. Above all,noise-contained original signal is reconstructed by Synchro-Squeezed Continuous Wavelet Transform(SS-CWT),of the high-energy noise de-noising by hard threshold. Then, useful information is remained during reverse conversion from high-low frequency,reasonable parameter selected to de-noise by costumed threshold Yoon-V. Compared with the traditional wavelet transform,this method has the advantage of high resolution,and it can extract the weak amplitude component of the signal more effectively than the Empirical Mode Decomposition(EMD). In the end,the characteristics of the signal before and after noise reduction are analyzed by SS-CWT time spectrum comparison,and the noise reduction effect is evaluated by the composite variable coefficient weighting T-value method. Keep effective information method in this paper is valid,obvious effect.
引文
Auger F,Flandrin P,Yu T L,et al.2013.Time-frequency reassignment and synchrosqueezing:an overview[J].IEEE Signal Processing Magazine,30(6):32-41.
BI Ming-xia,HUANG Han-ming,BIAN Yin-ju,et al.2012.Study on seismic signal features extraction based on EMD[J].Progress in Geophysics(in Chinese),27(5):1890-1896,doi:10.6038/j.issn.1004-2903.2012.05.008.
Brevdo E,Fucˇkar N S,Thakur G,et al.2011.The Synchrosqueezing algorithm:a robust analysis tool for signals with time-varying spectrum[J].IEEE Transactions on Signal Processing,93(5):1079-1094.
CAI Jian-hua,XIAO Xiao.2015.Method of processing magnetotelluric signal based on the adaptive threshold wavelet[J].Progress in Geophysics(in Chinese),30(6):2433-2439,doi:10.6038/pg20150601.
Chang S G,Yu B,Vetterli M.2000.Adaptive wavelet thresholding for image denoising and compression[J].IEEE Transactions on Image Processing,9(9):1532-1546.
Daubechies I.1992.Ten Lectures on Wavelets[M].Philadelphia,PA:Society for Industrial and Applied Mathematics.
Daubechies I,Lu J F,Wu H T.2011.Synchrosqueezed wavelet transforms:An empirical mode decomposition-like tool[J].Applied and Computational Harmonic Analysis,30(2):243-261.
Donoho D L.1995.Nonlinear solution of linear inverse problems by wavelet-vaguelette decomposition[J].Applied and Computational Harmonic Analysis,2(2):101-126.
Donoho D L,Johnstone J M.1994.Ideal spatial adaption by wavelet shrinkage[J].Biometrika,81(3):425-455.
Farge M.1992.Wavelet transforms and their applications to turbulence[J].Physics Today,24(1):395-457.
Grossmann A,Morlet J.1984.Decomposition of hardy functions into square integrable wavelets of constant shape[J].SIAM Journal on Mathematical Analysis,15(4):723-736.
Han J J,van der Baan M.2015.Microseismic and seismic denoising via ensemble empirical mode decomposition and adaptive thresholding[J].Geophysics,80(6):KS69-KS80.
Heil C E,Walnut D F.1989.Continuous and discrete wavelet transforms[J].SIAM Review,31(4):628-666.
Herrera R H,Han J J,van der Baan M.2014a.Applications of the synchrosqueezing transform in seismic time-frequency analysis[J].Geophysics,79(3):V55-V64.
Herrera R H,Tary J B,van der Baan M,et al.2014b.Body wave separation in the time-frequency domain[J].IEEE Geoscience and Remote Sensing Letters,12(2):364-368.
HUANG Lin.2017.Recent advance of acoustics for cracked porous media in identifying oil and gas[J].Progress in Geophysics(in Chinese),32(2):626-633,doi:10.6038/pg20170224.
JIA Rui-sheng,ZHAO Tong-bin,SUN Hong-mei,et al.2015.Microseismic signal denoising method based on empirical mode decomposition and independent component analysis[J].Chinese Journal of Geophysics(in Chinese),58(3):1013-1023,doi:10.6038/cjg20150326.
Li C,Liang M.2012.A generalized synchrosqueezing transform for enhancing signal time-frequency representation[J].Signal Processing,92(9):2264-2274.
LI Su-yi,JI Yan-ju,LIU Wei-yu,et al.2013.Application of wavelet transform and neural network in the near-infrared spectrum analysis of oil shale[J].Spectroscopy and Spectral Analysis(in Chinese),33(4):968-971.
LI Zong-chun,DENG Yong,ZHANG Guan-yu,et al.2011.Determination of best grading of wavelet transform in deformation measurement data filtering[J].Geomatics and Information Science of Wuhan University(in Chinese),36(3):285-288.
LIANG Bei-yuan,SHEN Chen,LENG Chuan-bo,et al.2015.Development of microseismic monitoring for hydro-fracturing[J].Progress in Geophysics(in Chinese),30(1):401-410,doi:10.6038/pg20150158.
LING Zhen-Bao,WANG Pei-Yuan,WAN Yun-Xia,et al.2016.Acombined wavelet transform algorithm used for de-noising magnetotellurics data in the strong human noise[J].Chinese Journal of Geophysics(in Chinese),59(9):3436-3447,doi:10.6038/cjg20160926.
Liu Jinhai.2016.New progress on monitoring and early warning technology ofmine strata pressure bump[J].Coal Science and Technology(in Chinese),44(6):71-77.
LüPeng-fei,CHEN Xue-hua,SONG Wei-hua,et al.2017.Mininginduced engineering effects base on microseismic monitoring and disaster-pregnant mechanism of shock bump[J].Progress in Geophysics(in Chinese),32(3):1399-1404,doi:10.6038/pg20170360.
Mallat S.1999.A Wavelet Tour of Signal Processing[M].2nd Ed.London:Academic Press.
Mendecki A J.1993.Real time quantitative seismology in mines[C].//Proceedings of 3rd International Symposium on Rockbursts and Seismicity in Mines.
Oth A,Parolai S,Bindi D,et al.2009.Source spectra and site response from s waves of intermediate-depth vrancea,romania,earthquakes[J].Bulletin of the Seismological Society of America,99(1):235-254.
Pearson C.1981.The relationship between microseismicity and high pore pressures during hydraulic stimulation experiments in low permeability granitic rocks[J].Journal of Geophysical Research:Solid Earth,86(B9):7855-7864.
QU Zhong-dang,WU Wei,HE Ri-zheng,et al.2015.Soft threshold filter based on S transform and its application to data processing of deep seismic reflection[J].Chinese Journal of Geophysics(in Chinese),58(9):3157-3168,doi:10.6038/cjg20150912.
Simpson D W,Leith W S,Scholz C H.1988.Two types of reservoirinduced seismicity[J].Bulletin of the Seismological Society of America,78(6):2025-2040.
Sobolev G A,Lyubushin A A.2006.Microseismic impulses as earthquake precursors[J].Izvestiya Physics of the Solid Earth,42(9):721-733.
Tary J B,Herrera R H,Han J J,et al.2014.Spectral estimation-What is new?What is next?[J].Reviews of Geophysics,52(4):723-749.
Thakur G,Brevdo E,Brevdo E,et al.2013.The Synchrosqueezing algorithm for time-varying spectral analysis:Robustness properties and new paleoclimate applications[J].Signal Processing,93(5):1079-1094.
Yoon B J,Vaidyanathan P P.2004.Wavelet-based denoising by customized thresholding[C].//Proceedings of 2004 IEEEInternational Conference on Acoustics,Speech,and Signal Processing.Montreal,Que.,Canada:IEEE.
ZHU Jian-jun,ZHANG Zhe-tao,KUANG Cui-lin,et alo.2015.Areliable evaluation indicator of wavelet de-noising[J].Geomatics and Information Science of Wuhan University(in Chinese),40(5):688-694.
毕明霞,黄汉明,边银菊,等.2012.基于经验模态分解的地震波特征提取的研究[J].地球物理学进展,27(5):1890-1896,doi:10.6038/j.issn.1004-2903.2012.05.008.
蔡剑华,肖晓.2015.基于小波自适应阈值去噪的MT信号处理方法[J].地球物理学进展,30(6):2433-2439,doi:10.6038/pg20150601.
黄琳.2017.孔、裂隙介质声学理论及其在油气识别应用中的新进展[J].地球物理学进展,32(2):626-633,doi:10.6038/pg20170224.
贾瑞生,赵同彬,孙红梅,等.2015.基于经验模态分解及独立成分分析的微震信号降噪方法[J].地球物理学报,58(3):1013-1023,doi:10.6038/cjg20150326.
梁北援,沈琛,冷传波,等.2015.微地震压裂监测技术研发进展[J].地球物理学进展,30(1):401-410,doi:10.6038/pg20150158.
凌振宝,王沛元,万云霞,等.2016.强人文干扰环境的电磁数据小波去噪方法研究[J].地球物理学报,59(9):3436-3447,doi:10.6038/cjg20160926.
刘金海.2016.煤矿冲击地压监测预警技术新进展[J].煤炭科学技术,44(6):71-77.
李肃义,嵇艳鞠,刘伟宇,等.2013.小波变换与神经网络融合法在油页岩近红外光谱分析中的应用[J].光谱学与光谱分析,33(4):968-971.
李宗春,邓勇,张冠宇,等.2011.变形测量异常数据处理中小波变换最佳级数的确定[J].武汉大学学报·信息科学版,36(3):285-288.
吕鹏飞,陈学华,宋卫华,等.2017.基于微震监测的采动工程效应及矿震孕灾机理[J].地球物理学进展,32(3):1399-1404,doi:10.6038/pg20170360.
曲中党,吴蔚,贺日政,等.2015.基于S变换的软阈值滤波在深地震反射数据处理中的应用[J].地球物理学报,58(9):3157-3168,doi:10.6038/cjg20150912.
朱建军,章浙涛,匡翠林,等.2015.一种可靠的小波去噪质量评价指标[J].武汉大学学报·信息科学版,40(5):688-694.
BI Ming-xia,HUANG Han-ming,BIAN Yin-ju,et al.2012.Study on seismic signal features extraction based on EMD[J].Progress in Geophysics(in Chinese),27(5):1890-1896,doi:10.6038/j.issn.1004-2903.2012.05.008.
Brevdo E,Fucˇkar N S,Thakur G,et al.2011.The Synchrosqueezing algorithm:a robust analysis tool for signals with time-varying spectrum[J].IEEE Transactions on Signal Processing,93(5):1079-1094.
CAI Jian-hua,XIAO Xiao.2015.Method of processing magnetotelluric signal based on the adaptive threshold wavelet[J].Progress in Geophysics(in Chinese),30(6):2433-2439,doi:10.6038/pg20150601.
Chang S G,Yu B,Vetterli M.2000.Adaptive wavelet thresholding for image denoising and compression[J].IEEE Transactions on Image Processing,9(9):1532-1546.
Daubechies I.1992.Ten Lectures on Wavelets[M].Philadelphia,PA:Society for Industrial and Applied Mathematics.
Daubechies I,Lu J F,Wu H T.2011.Synchrosqueezed wavelet transforms:An empirical mode decomposition-like tool[J].Applied and Computational Harmonic Analysis,30(2):243-261.
Donoho D L.1995.Nonlinear solution of linear inverse problems by wavelet-vaguelette decomposition[J].Applied and Computational Harmonic Analysis,2(2):101-126.
Donoho D L,Johnstone J M.1994.Ideal spatial adaption by wavelet shrinkage[J].Biometrika,81(3):425-455.
Farge M.1992.Wavelet transforms and their applications to turbulence[J].Physics Today,24(1):395-457.
Grossmann A,Morlet J.1984.Decomposition of hardy functions into square integrable wavelets of constant shape[J].SIAM Journal on Mathematical Analysis,15(4):723-736.
Han J J,van der Baan M.2015.Microseismic and seismic denoising via ensemble empirical mode decomposition and adaptive thresholding[J].Geophysics,80(6):KS69-KS80.
Heil C E,Walnut D F.1989.Continuous and discrete wavelet transforms[J].SIAM Review,31(4):628-666.
Herrera R H,Han J J,van der Baan M.2014a.Applications of the synchrosqueezing transform in seismic time-frequency analysis[J].Geophysics,79(3):V55-V64.
Herrera R H,Tary J B,van der Baan M,et al.2014b.Body wave separation in the time-frequency domain[J].IEEE Geoscience and Remote Sensing Letters,12(2):364-368.
HUANG Lin.2017.Recent advance of acoustics for cracked porous media in identifying oil and gas[J].Progress in Geophysics(in Chinese),32(2):626-633,doi:10.6038/pg20170224.
JIA Rui-sheng,ZHAO Tong-bin,SUN Hong-mei,et al.2015.Microseismic signal denoising method based on empirical mode decomposition and independent component analysis[J].Chinese Journal of Geophysics(in Chinese),58(3):1013-1023,doi:10.6038/cjg20150326.
Li C,Liang M.2012.A generalized synchrosqueezing transform for enhancing signal time-frequency representation[J].Signal Processing,92(9):2264-2274.
LI Su-yi,JI Yan-ju,LIU Wei-yu,et al.2013.Application of wavelet transform and neural network in the near-infrared spectrum analysis of oil shale[J].Spectroscopy and Spectral Analysis(in Chinese),33(4):968-971.
LI Zong-chun,DENG Yong,ZHANG Guan-yu,et al.2011.Determination of best grading of wavelet transform in deformation measurement data filtering[J].Geomatics and Information Science of Wuhan University(in Chinese),36(3):285-288.
LIANG Bei-yuan,SHEN Chen,LENG Chuan-bo,et al.2015.Development of microseismic monitoring for hydro-fracturing[J].Progress in Geophysics(in Chinese),30(1):401-410,doi:10.6038/pg20150158.
LING Zhen-Bao,WANG Pei-Yuan,WAN Yun-Xia,et al.2016.Acombined wavelet transform algorithm used for de-noising magnetotellurics data in the strong human noise[J].Chinese Journal of Geophysics(in Chinese),59(9):3436-3447,doi:10.6038/cjg20160926.
Liu Jinhai.2016.New progress on monitoring and early warning technology ofmine strata pressure bump[J].Coal Science and Technology(in Chinese),44(6):71-77.
LüPeng-fei,CHEN Xue-hua,SONG Wei-hua,et al.2017.Mininginduced engineering effects base on microseismic monitoring and disaster-pregnant mechanism of shock bump[J].Progress in Geophysics(in Chinese),32(3):1399-1404,doi:10.6038/pg20170360.
Mallat S.1999.A Wavelet Tour of Signal Processing[M].2nd Ed.London:Academic Press.
Mendecki A J.1993.Real time quantitative seismology in mines[C].//Proceedings of 3rd International Symposium on Rockbursts and Seismicity in Mines.
Oth A,Parolai S,Bindi D,et al.2009.Source spectra and site response from s waves of intermediate-depth vrancea,romania,earthquakes[J].Bulletin of the Seismological Society of America,99(1):235-254.
Pearson C.1981.The relationship between microseismicity and high pore pressures during hydraulic stimulation experiments in low permeability granitic rocks[J].Journal of Geophysical Research:Solid Earth,86(B9):7855-7864.
QU Zhong-dang,WU Wei,HE Ri-zheng,et al.2015.Soft threshold filter based on S transform and its application to data processing of deep seismic reflection[J].Chinese Journal of Geophysics(in Chinese),58(9):3157-3168,doi:10.6038/cjg20150912.
Simpson D W,Leith W S,Scholz C H.1988.Two types of reservoirinduced seismicity[J].Bulletin of the Seismological Society of America,78(6):2025-2040.
Sobolev G A,Lyubushin A A.2006.Microseismic impulses as earthquake precursors[J].Izvestiya Physics of the Solid Earth,42(9):721-733.
Tary J B,Herrera R H,Han J J,et al.2014.Spectral estimation-What is new?What is next?[J].Reviews of Geophysics,52(4):723-749.
Thakur G,Brevdo E,Brevdo E,et al.2013.The Synchrosqueezing algorithm for time-varying spectral analysis:Robustness properties and new paleoclimate applications[J].Signal Processing,93(5):1079-1094.
Yoon B J,Vaidyanathan P P.2004.Wavelet-based denoising by customized thresholding[C].//Proceedings of 2004 IEEEInternational Conference on Acoustics,Speech,and Signal Processing.Montreal,Que.,Canada:IEEE.
ZHU Jian-jun,ZHANG Zhe-tao,KUANG Cui-lin,et alo.2015.Areliable evaluation indicator of wavelet de-noising[J].Geomatics and Information Science of Wuhan University(in Chinese),40(5):688-694.
毕明霞,黄汉明,边银菊,等.2012.基于经验模态分解的地震波特征提取的研究[J].地球物理学进展,27(5):1890-1896,doi:10.6038/j.issn.1004-2903.2012.05.008.
蔡剑华,肖晓.2015.基于小波自适应阈值去噪的MT信号处理方法[J].地球物理学进展,30(6):2433-2439,doi:10.6038/pg20150601.
黄琳.2017.孔、裂隙介质声学理论及其在油气识别应用中的新进展[J].地球物理学进展,32(2):626-633,doi:10.6038/pg20170224.
贾瑞生,赵同彬,孙红梅,等.2015.基于经验模态分解及独立成分分析的微震信号降噪方法[J].地球物理学报,58(3):1013-1023,doi:10.6038/cjg20150326.
梁北援,沈琛,冷传波,等.2015.微地震压裂监测技术研发进展[J].地球物理学进展,30(1):401-410,doi:10.6038/pg20150158.
凌振宝,王沛元,万云霞,等.2016.强人文干扰环境的电磁数据小波去噪方法研究[J].地球物理学报,59(9):3436-3447,doi:10.6038/cjg20160926.
刘金海.2016.煤矿冲击地压监测预警技术新进展[J].煤炭科学技术,44(6):71-77.
李肃义,嵇艳鞠,刘伟宇,等.2013.小波变换与神经网络融合法在油页岩近红外光谱分析中的应用[J].光谱学与光谱分析,33(4):968-971.
李宗春,邓勇,张冠宇,等.2011.变形测量异常数据处理中小波变换最佳级数的确定[J].武汉大学学报·信息科学版,36(3):285-288.
吕鹏飞,陈学华,宋卫华,等.2017.基于微震监测的采动工程效应及矿震孕灾机理[J].地球物理学进展,32(3):1399-1404,doi:10.6038/pg20170360.
曲中党,吴蔚,贺日政,等.2015.基于S变换的软阈值滤波在深地震反射数据处理中的应用[J].地球物理学报,58(9):3157-3168,doi:10.6038/cjg20150912.
朱建军,章浙涛,匡翠林,等.2015.一种可靠的小波去噪质量评价指标[J].武汉大学学报·信息科学版,40(5):688-694.