基于小波包分析和SVM的爆破震动与岩石破裂微震信号的识别研究
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摘要
为提高爆破震动与岩石破裂微震信号辨识精度,以某金属矿山现场微震监测数据为基础,首先采用小波包分解对矿山爆破震动和岩石破裂信号进行2层分解,对比两类信号在4个频段内的能量分布的特征,进而以两类事件的低频段(0～125 Hz)能量百分比、中低频段(125～250 Hz)量百分比、中高频段(250～375 Hz)能量百分比、高频段(375～500 Hz)能量百分比为特征向量,利用支持向量机(SVM)对爆破和岩石破裂微震信号进行了训练和分类,结果表明:两类事件在0～125 Hz的能量分布差异最大,且以0～125 Hz的能量百分比10%作为分界值时的准确率达到87.5%;SVM的分类正确率为94%,取得了理想的分类效果。
In order to improve identification accuracy of the blasting vibration and rock fracture micro-seismic signal, based on in-situ micro-seismic data,the micro-seismic signals were decomposed into 2 multi-scale,4 frequency bands to calculate the signals energy under different bands and their energy distribution is different. Then through using energy percent of low frequency(0～125Hz), medium-low frequency(125～250Hz), upper-middle(250～375Hz) frequency, high frequency(375～500Hz) as feature vector, SVM is adopted to train, classify the signals. Result shows that there are largest differences of energy distribution in low frequency(0～125Hz)between microseisms and blasts,and the best pattern recognition is obtained when energy percent of low frequency(0～125H)is 10% with an accuracy rate of 87.5%;The correct classified rate by SVM is 94%
In order to improve identification accuracy of the blasting vibration and rock fracture micro-seismic signal, based on in-situ micro-seismic data,the micro-seismic signals were decomposed into 2 multi-scale,4 frequency bands to calculate the signals energy under different bands and their energy distribution is different. Then through using energy percent of low frequency(0～125Hz), medium-low frequency(125～250Hz), upper-middle(250～375Hz) frequency, high frequency(375～500Hz) as feature vector, SVM is adopted to train, classify the signals. Result shows that there are largest differences of energy distribution in low frequency(0～125Hz)between microseisms and blasts,and the best pattern recognition is obtained when energy percent of low frequency(0～125H)is 10% with an accuracy rate of 87.5%;The correct classified rate by SVM is 94%
引文
[1] 朱权洁,姜福兴,于正兴等.爆破震动与岩石破裂微震信号能量分布特征研究[J].岩石力学与工程学报,2012,31(4):723-730.
[2] 尚雪义,李夕兵,彭康,等.基于EMD-SVD的矿山微震与爆破特征提取及分类方法[J].岩土工程学报 2016,38(10):1849-1858.
[3] 朱权洁,姜福兴,尹永明,等.基于小波分形特征与模式识别的矿山微震波形识别研究[J].岩土工程学报, 2012,34(11):2036–2042.
[4] 董陇军,孙道元,李夕兵,等.微震与爆破时间统计识别方法及工程应用[J].岩土力学与工程学报,2016,35(7):1423-1433.
[5] MALOVICHKO D. Discrimination of blasts in mine seismology; proceedings of the Deep Mining 2012[C].Australian Centre for Geomechanics,Perth,F,2012
[6] DONG L J,WESSELOO J,YVES P,et al. Discriminate models of blasts and seismic events in mine seismology[J]. International Journal of Rock Mechanics and Mining Sciences,2016,86:282–291.
[7] 赵国彦,邓青林,马举.基于FSWT时频分析的矿山微震信号分析与识别[J].岩土工程学报,2015, 37(2): 306–312.
[8] 唐守锋,童敏明,潘玉祥,等.煤岩破裂微震信号的小波特征能谱系数分析法[J].仪器仪表学报,2011, 32(7): 1521–1527.
[9] 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.
[10] ALLMANN B P, SHEARER P M, HAUKSSON E. Spectral discrimination between quarry blasts and earthquakes in Southern California[J]. Bulletin of the Seismological Society of America, 2008, 98(4): 2073–2079.
[11] 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.
[12] 徐飞.基于小波变换的爆破振动信号分析方法及应用[J].淮南:安徽理工大学,2011.
[13] 宋广东,刘统玉,王昌,等.小波分析在微地震信号处理中的应用研究[J].山东科学,2011,24(3):64-69.
[14] 丁福利,孙立民.基于支持向量机的不平衡分类研究[J]科学技术与工程,2014,14(3):81-85.
[15] 凌同华,李夕兵.爆破振动信号不同频带的能量分布规律[J].中南大学学报(自然科学版),2004,35(2):310-315.
[2] 尚雪义,李夕兵,彭康,等.基于EMD-SVD的矿山微震与爆破特征提取及分类方法[J].岩土工程学报 2016,38(10):1849-1858.
[3] 朱权洁,姜福兴,尹永明,等.基于小波分形特征与模式识别的矿山微震波形识别研究[J].岩土工程学报, 2012,34(11):2036–2042.
[4] 董陇军,孙道元,李夕兵,等.微震与爆破时间统计识别方法及工程应用[J].岩土力学与工程学报,2016,35(7):1423-1433.
[5] MALOVICHKO D. Discrimination of blasts in mine seismology; proceedings of the Deep Mining 2012[C].Australian Centre for Geomechanics,Perth,F,2012
[6] DONG L J,WESSELOO J,YVES P,et al. Discriminate models of blasts and seismic events in mine seismology[J]. International Journal of Rock Mechanics and Mining Sciences,2016,86:282–291.
[7] 赵国彦,邓青林,马举.基于FSWT时频分析的矿山微震信号分析与识别[J].岩土工程学报,2015, 37(2): 306–312.
[8] 唐守锋,童敏明,潘玉祥,等.煤岩破裂微震信号的小波特征能谱系数分析法[J].仪器仪表学报,2011, 32(7): 1521–1527.
[9] 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.
[10] ALLMANN B P, SHEARER P M, HAUKSSON E. Spectral discrimination between quarry blasts and earthquakes in Southern California[J]. Bulletin of the Seismological Society of America, 2008, 98(4): 2073–2079.
[11] 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.
[12] 徐飞.基于小波变换的爆破振动信号分析方法及应用[J].淮南:安徽理工大学,2011.
[13] 宋广东,刘统玉,王昌,等.小波分析在微地震信号处理中的应用研究[J].山东科学,2011,24(3):64-69.
[14] 丁福利,孙立民.基于支持向量机的不平衡分类研究[J]科学技术与工程,2014,14(3):81-85.
[15] 凌同华,李夕兵.爆破振动信号不同频带的能量分布规律[J].中南大学学报(自然科学版),2004,35(2):310-315.