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矿山微震信号小波分析与研究
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唐礼忠, 陈资南, 张君, 高龙华
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    中南大学资源与安全工程学院, 长沙 410083
Research and Analysis on Wavelet of Mine Microseismic Signals
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出版时间: 2013-11-18 doi: 10.3981/j.issn.1000-7857.2013.32.004
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针对目前人工信号识别技术和傅里叶变换在分析识别矿山微震信号时的局限性,提出Matlab小波工具箱分析方法。首先对矿山实际监测到的干扰矿震信号进行小波变换,对小波分解之后的矿震信号进行频谱分析,结果表明能够准确地观察到信号的突变点,确定矿震信号P波初到时,从而更加精确得出矿震震源的位置和能量大小;其次对受干扰矿震信号进行小波消噪,同时对小波分析的4种阈值降噪方法进行分析对比,发现无偏似然估计阈值效果最好,显示了小波分析的强大消噪功能。研究结果表明,小波分析具有良好的矿震信号识别效果和消噪能力,是矿震信号处理和分析的一种有效方法。
矿震信号  /  小波变换  /  小波消噪  /  P波初到时
Due to the limitations of the present artificial signal recognition technology and Fourier transform in analyzing the mine microseismic signal, the Matlab wavelet toolbox analysis method was presented. Through the transformation of the microseismic signals monitored in mine, prior to the spectral analysis of transformation signals, the discontinuity of the signals were accurately observed and the first arrival time of P wave was determined. As a result, the accurate location and energy of mining shocks were concluded. Further, through the wavelet de-noising of signals and comparing the four kinds of de-noising methods, the unbiased estimate threshold method worked best and this showed the powerful function of the wavelet analysis in de-noising. Therefore, it is revealed that the wavelet analysis is an effective method of seismic signal processing and analysis as it is capable of seismic signal recognition and noise elimination.
mine microseismic signal  /  wavelet transformation  /  wavelet de-noising  /  first arrival times of P wave
唐礼忠;陈资南;张君;高龙华. 矿山微震信号小波分析与研究. 科技导报, 2013 , 31 (32) : 29 -33 . DOI: 10.3981/j.issn.1000-7857.2013.32.004
TANG Lizhong;CHEN Zinan;ZHANG Jun;GAO Longhua. Research and Analysis on Wavelet of Mine Microseismic Signals[J]. Science & Technology Review, 2013 , 31 (32) : 29 -33 . DOI: 10.3981/j.issn.1000-7857.2013.32.004
2013年第31卷第32期
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doi: 10.3981/j.issn.1000-7857.2013.32.004
  • 接收时间:2013-06-03
  • 首发时间:2013-11-18
  • 出版时间:2013-11-18
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  • 收稿日期:2013-06-03
  • 修回日期:2013-10-08
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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