FFT频谱分析在微震信号识别中的应用
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摘要
为识别采场大爆破信号与岩石破裂大震级微震信号,运用MATLAB的快速傅里叶变换(FFT)频谱分析,对采场大爆破信号和大震级微震信号的功率谱和幅频特性进行分析,并对比两者能量在频带上的分布差异。研究表明,大震级微震信号频带分布较宽,且在30~50 Hz达到了最大幅值,采场大爆破信号频带更窄、幅值更大、且一般在10 Hz就能达到最大幅值。由于爆破释放的能量较大且释放的非常快,采场大爆破信号能量大多分布在0~30 Hz的低频区域,大震级微震信号的能量大多分布于30~50 Hz区域。利用信号特性实现对两种信号快速有效的辨识,为后期微震监测对地压风险区域的预测预报提供了准确的数据支撑。
To identify large scale stopping blasting and large magnitude micro-seismic signals, the FFT spectral analysis method is used. The analysis of the stopping blasting and macro-scale rock fracture signals is made through power spectrum and magnitudefrequency characteristics. The distribution difference of the energy on the frequency brand can be revealed. It is shown that in the frequency brand distribution of the large magnitude micro-seismic signal, the amplitude value takes the maximum at about 10 Hz and30~50 Hz. The large scale stopping blasting is in a narrow frequency brand. Its amplitude value is higher than the normal signal. It reaches the top amplitude at 10 Hz. From the energy distribution, the energy distribution of the blasting signal mostly in the 0-30 Hz low frequency area. It is because the blasting is always accompanied with an enormous energy, which is released very fast. The microseismic energy is distributed mostly in the range of 30-50 Hz. So for the two kinds of signal identifications, two kinds of signal spectrum characteristics may be used.
To identify large scale stopping blasting and large magnitude micro-seismic signals, the FFT spectral analysis method is used. The analysis of the stopping blasting and macro-scale rock fracture signals is made through power spectrum and magnitudefrequency characteristics. The distribution difference of the energy on the frequency brand can be revealed. It is shown that in the frequency brand distribution of the large magnitude micro-seismic signal, the amplitude value takes the maximum at about 10 Hz and30~50 Hz. The large scale stopping blasting is in a narrow frequency brand. Its amplitude value is higher than the normal signal. It reaches the top amplitude at 10 Hz. From the energy distribution, the energy distribution of the blasting signal mostly in the 0-30 Hz low frequency area. It is because the blasting is always accompanied with an enormous energy, which is released very fast. The microseismic energy is distributed mostly in the range of 30-50 Hz. So for the two kinds of signal identifications, two kinds of signal spectrum characteristics may be used.
引文
[1]刘建坡.深井矿山地压活动与微震时空演化关系研究[D].沈阳:东北大学,2011.Liu Jianpo.Studies on relationship between microseism time-spaceevolution and ground pressure aetivities in deep mine[D].Shenyang:Northeastern University,2011.
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[8]袁瑞甫,李化敏,李怀珍.煤柱型冲击地压微震信号分布特征及前兆信息判别[J].岩石力学与工程学报,2012,31(1):80-85.Yuan Ruifu,Li Huamin,Li Huaizhen.Distribution of microseismicsignal and discrimination of portentous information of pillar typerockburst[J].Chinese Journal of Rock Mechanics and Engineering,2012,31(1):80-85.
[9]大崎顺彥.地震动的谱分析入门[M].吕敏申,谢礼立,译.北京:地震出版社,1980.Ozaki Nobuhiko.Introduction to spectral analysis of ground motion[M].LüMinshen,Xie Lili,trans.Beijing:Seismological Press,1980.
[10]李志华,窦林名,陆菜平.断层冲击相似模拟微震信号频谱分析[J].山东科技大学学报,2010,29(4):51-56.Li Zhihua,Dou Linming,Lu Caiping.Frequency spectrum analysisonmicro-seismic signalof similar simulation test of fault rock burst[J].Journal of Shandong University of Science and Technology,2010,29(4):51-56.
[11]Gibowicz S J,Kijko A.矿山地震学引论[M].修济刚,译.北京:地震出版社,1998.Gibowicz S J,Kijko A.Introduction to mining seismology[M].XiuJigang,trans.Beijing:Seismological Press,1998.
[12]胡丽莹,肖蓬.快速傅里叶变换在频谱分析中的应用[J].福建师范大学学报,2011,27(4):27-30.Hu Liying,Xiao Peng.Application of fast fourier transform infrequency and spectrum analysis[J].Journal of Fujian NormalUniversity,2011,27(4):27-30.
[2]徐奴文.高陡岩质边坡微震监测与稳定性分析研究[D].大连:大连理工大学,2011.Xu Nuwen.Study on microseismic monitoring and stability analysis ofhigh steep rock slope[D].Dalian:Dalian University of Technology,2011.
[3]赵毅鑫,姜耀东,王涛,等.“两硬”条件下冲击地压微震信号特征及前兆识别[J].煤炭学报,2012,37(12):1960-1966.Zhao Yixin,Jiang Yaodong,Wang Tao,et al.Features of microseismicevents and precursors of rock burst in underground coal mining withhard roof[J].Journal of China Coal Society,2012,37(12):1960-1966.
[4]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):772-775.Lu Caiping,Dou Linming,Wu Xingrong,et al.Frequency spectrumanalysis on microseismic monitoring and signal differentiation of rockmaterial[J].Chinese Journal of Geotechnical Engineering,2005,27(7):772-775.
[5]曹安业,窦林名,秦玉红,等.高应力区微震监测信号特征分析[J].采矿与安全工程学报,2007,24(2):146-149.Cao Anye,Dou Linmin,Qin Yuhong.Characteristic of microseismicmonitoring signal in high stressed zone[J].Journal of Mining&SafetyEngineering,2007,24(2):146-149.
[6]曹华锋,林峰.微震监测信号辨识方法研究[J].采矿技术,2011,11(2):55-58.Cao Huafeng,Lin Feng.Study on microseismic monitoring signalidentification method[J].Mining Technology,2011,11(2):55-58.
[7]唐礼忠,陈资南,张君,等.矿山微震信号小波分析与研究[J].科技导报,2013,31(31):29-33.Tang Lizhong,Chen Zinan,Zhang Jun,et al.Research and analysis onwavelet of mine microseeismic signals[J].Science&TechnologyReview,2013,31(31):29-33.
[8]袁瑞甫,李化敏,李怀珍.煤柱型冲击地压微震信号分布特征及前兆信息判别[J].岩石力学与工程学报,2012,31(1):80-85.Yuan Ruifu,Li Huamin,Li Huaizhen.Distribution of microseismicsignal and discrimination of portentous information of pillar typerockburst[J].Chinese Journal of Rock Mechanics and Engineering,2012,31(1):80-85.
[9]大崎顺彥.地震动的谱分析入门[M].吕敏申,谢礼立,译.北京:地震出版社,1980.Ozaki Nobuhiko.Introduction to spectral analysis of ground motion[M].LüMinshen,Xie Lili,trans.Beijing:Seismological Press,1980.
[10]李志华,窦林名,陆菜平.断层冲击相似模拟微震信号频谱分析[J].山东科技大学学报,2010,29(4):51-56.Li Zhihua,Dou Linming,Lu Caiping.Frequency spectrum analysisonmicro-seismic signalof similar simulation test of fault rock burst[J].Journal of Shandong University of Science and Technology,2010,29(4):51-56.
[11]Gibowicz S J,Kijko A.矿山地震学引论[M].修济刚,译.北京:地震出版社,1998.Gibowicz S J,Kijko A.Introduction to mining seismology[M].XiuJigang,trans.Beijing:Seismological Press,1998.
[12]胡丽莹,肖蓬.快速傅里叶变换在频谱分析中的应用[J].福建师范大学学报,2011,27(4):27-30.Hu Liying,Xiao Peng.Application of fast fourier transform infrequency and spectrum analysis[J].Journal of Fujian NormalUniversity,2011,27(4):27-30.
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