煤岩破裂微震信号的小波特征能谱系数分析法
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摘要
煤岩破裂微震信号是一种时变非平稳信号,具有不可预知性和突发瞬态性,为了提取能够表征微震源信息的有效特征,基于小波理论,选取了适合微震信号的小波基函数,并根据采样定理及Mallat算法确定了小波分解最大尺度,提出了用小波特征能谱系数分析煤岩破裂微震信号波形的状态分析方法。实验结果表明,微震信号与噪声信号有明显不同的小波特征能谱系数分布特征,而且可利用提取的能谱系数作为特征向量表示信号特征。这对于抑制微震信号的不稳定性和同频段的干扰信号,以及后续的特征编码非常有利,为实现煤岩破裂微震信号实时的模式识别打下了重要基础。
Coal rupture microseismic signal is characterized by time-varying,non-stationary,unpredictable and transient properties.To extract effective features representing microseismic source information,the wavelet basis function suitable for microseismic signal is selected based on wavelet theory.Besides,the maximum wavelet decomposition dimension is determined according to sampling theorem and Mallat algorithm.Furthermore,energy spectrum coefficients of wavelet features are proposed to analyze coal rupture microseismic signal.Experimental results indicate that microseismic signal and noise have different characteristics,which can be effectively demonstrated by energy spectrum coefficient of wavelet features.Moreover,fewer parameters can be used to analyze and characterize the signal in simple and direct-viewing ways.The above research is beneficial to suppress the interference signals in same frequency and the instability of microseismic signal,as well as is advantageous to subsequent feature encoding.It also lays an important foundation for real-time pattern recognition of coal rupture microseismic signal.
Coal rupture microseismic signal is characterized by time-varying,non-stationary,unpredictable and transient properties.To extract effective features representing microseismic source information,the wavelet basis function suitable for microseismic signal is selected based on wavelet theory.Besides,the maximum wavelet decomposition dimension is determined according to sampling theorem and Mallat algorithm.Furthermore,energy spectrum coefficients of wavelet features are proposed to analyze coal rupture microseismic signal.Experimental results indicate that microseismic signal and noise have different characteristics,which can be effectively demonstrated by energy spectrum coefficient of wavelet features.Moreover,fewer parameters can be used to analyze and characterize the signal in simple and direct-viewing ways.The above research is beneficial to suppress the interference signals in same frequency and the instability of microseismic signal,as well as is advantageous to subsequent feature encoding.It also lays an important foundation for real-time pattern recognition of coal rupture microseismic signal.
引文
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[13]钱华明,王雯升.遗传小波神经网络及在电机故障诊断中的应用[J].电子测量与仪器学报,2009,23(3):81-86.QIAN H M,WANG W SH.Improved wavelet neural net-work based on genetic algorithm and its application infault diagnosis of motor[J].Journal of Electronic Meas-urement and Instrument,2009,23(3):81-86.
[14]刘金华,佘堃.一种采用小波滤波的独立分量分析算法[J].电子测量与仪器学报,2010(1):39-44.LIU J H,SHE K.Independent component analysis algo-rithm using wavelet filtering[J].Journal of ElectronicMeasurement and Instrument,2010(1):39-44.
[15]张平.集成化声发射信号处理平台的研究[D].博士学位论文,清华大学机械工程系,2002.ZHANG P.Studies on integrated platform for acousticemission signal processing[D].Ph.D.Thesis,TsinghuaUniversity,2002.
[2]陆菜平,窦林名,吴兴荣,等.煤岩冲击前兆微震频谱演变规律的试验与实证研究[J].岩石力学与工程学报,2008,27(3):519-525.LU C P,DOU L M,WU X R,et al.Experimental andempirical research on frequency-spectrum evolvement ruleof rockburst precursory microseismic signals of coal-rock[J].Chinese Journal of Rock Mechanics and Engineer-ing,2008,27(3):519-525.
[3]姜福兴.微震监测技术在矿井岩层破裂监测中的应用[J].岩土工程学报,2002,24(2):147-149.JIANG F X.Application of microseismic monitoring tech-nology of strata fracturing in underground coal mine[J].Chinese Jounal of Geotechnical Engineering,2002,24(2):147-149.
[4]展建设,曾克,曹修定,等.以微震特征监测地质灾害的实验研究[J].勘察科学技术,2002,1:61-64.ZHAN J SH,ZENG K,CAO X D,et al.The experimen-tal research into the monitoring of geological hazard withthe microseismic characteristic[J].Site InvestigationScience and Technology,2002,1:61-64.
[5]刘百红,秦绪英,郑四连,等.微地震监测技术及其在油田中的应用现状[J].勘探地球物理进展,2005,28(5):325-329.LIU B H,QIN X Y,ZHENG S L,et al.Microseismicmonitoring and its applications in oilfield operations[J].Progress in Exploration Geophysics,2005,28(5):325-329.
[6]沈功田,耿荣生,刘时风.声发射信号的参数分析方法[J].无损检测,2002,24(2):72-77.SHEN G T,GENG R SH,LIU SH F.Parameter analysisof acoustic emission signals[J].Nonde Structive Tes-ting,2002,24(2):72-77.
[7]王维,张英堂,任国全.小波阈值降噪算法中最优分解层数的自适应确定及仿真[J].仪器仪表学报,2009(3):526-530.WANG W,ZHANG Y T,REN G Q.Adaptive selectionand simulation of optimal decomposition level in thresholdde-noising algorithm based on wavelet transform[J].Chi-nese Journal of Scientific Instrument,2009(3):526-530.
[8]陆菜平,窦林名,吴兴荣,等.岩体微震监测的频谱分析与信号识别[J].岩土工程学报,2005,27(7):773-775.LU C P,DOU L M,WU X R,et al.Frequency spectrumanalysis on microseismic monitoring and signal differentia-tion of rock material[J].Chinese Journal of GeotechnicalEngineering,2005,27(7):773-775.
[9]CARPINTERI A,LACIDOGNA G,PUGNO N.Structuraldamage diagnosis and life-time assessment by acoustic emis-sion monitoring[J].Engineering Fracture Mechanics,Vol-ume 74,Issues 1-2,January 2007,74(1):273-289.
[10]杨进,文玉梅,李平.基于相关分析和近似熵的管道泄漏声信号特征提取及辨识方法[J].仪器仪表学报,2009(2):272-279.YANG J,WEN Y M,LI P.Feature extraction and iden-tification of leak acoustic signal in water distribution pipe-lines using correlation analysis and approximate entropy[J].Chinese Journal of Scientific Instrument,2009(2):272-279.
[11]LIAO T W.Feature extraction and selection from acousticemission signals with an application in grinding wheelcondition monitoring[J].Engineering Applications of Ar-tificial Intelligence,2010,23(2):74-84.
[12]边海龙,陈光礻禹,李林,等.测试系统中非平稳信号的时频优化小波包检测算法[J].仪器仪表学报,2009(3):498-502.BIAN H L,CHEN G J,LI L,et al.Measurement algo-rithm of nonstationary signal based on time and frequencyoptimization using wavelet packet transform[J].ChineseJournal of Scientific Instrument,2009(3):498-502.
[13]钱华明,王雯升.遗传小波神经网络及在电机故障诊断中的应用[J].电子测量与仪器学报,2009,23(3):81-86.QIAN H M,WANG W SH.Improved wavelet neural net-work based on genetic algorithm and its application infault diagnosis of motor[J].Journal of Electronic Meas-urement and Instrument,2009,23(3):81-86.
[14]刘金华,佘堃.一种采用小波滤波的独立分量分析算法[J].电子测量与仪器学报,2010(1):39-44.LIU J H,SHE K.Independent component analysis algo-rithm using wavelet filtering[J].Journal of ElectronicMeasurement and Instrument,2010(1):39-44.
[15]张平.集成化声发射信号处理平台的研究[D].博士学位论文,清华大学机械工程系,2002.ZHANG P.Studies on integrated platform for acousticemission signal processing[D].Ph.D.Thesis,TsinghuaUniversity,2002.
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