Sound signal analysis for warning and intensity evaluation of rockburst
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摘要: 利用真三轴岩爆实验系统在室内再现了岩爆动力破坏过程，采用数字录音笔对岩爆过程的声音信号进行监测，在声音信号预处理的基础上，对岩爆过程中的颗粒弹射、岩板劈裂、块片弹射3种岩石脆性破坏现象的不同声音特征指标进行分析。结果表明：3种典型脆性破坏现象的声音信号在波形、频谱、声纹和短时能量等特性指标上存在明显差异，这些特征指标适用于岩爆的特征提取。提出了一种基于声音信号的岩爆烈度评价指标——局部声响总能量，该指标适用于定量评价岩爆发生的剧烈程度。Abstract: The rockburst process was reproduced using a true-triaixal rockburst test system in laboratory. The sound signal in the rockburst process was recorded and preprocessed, and the feature indexes of the sound signals in three typical failures in the rockburst process, including rock particles ejection, rock splitting, and rock plate ejection, were investigated. The results show that the feature indexes in the three typical failures such as the waveform, the spectrum, the sound print and the short-term energy exhibit significant differences from each other. Finally, an indicator called total energy of local sound (TELS) was proposed as applicable to assessing the rockburst intensity.
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Key words:
- rock engineering /
- rockburst warning /
- rockburst intensity /
- sound signal
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图 3 3种典型破坏现象的声音信号波形
Figure 3. Waveforms of sound signals for three typical failure phenomena
图 4 3种典型破坏现象的声音信号频谱
Figure 4. Spectra of sound signals of three typical failure phenomena
图 5 3种典型破坏现象的声音信号声纹
Figure 5. Voiceprints of sound signals for three typical failure phenomena
图 6 3种典型破坏现象的声音信号短时能量
Figure 6. Short-time energies of sound signals for three typical failure phenomena
图 7 不同等级岩爆短时能量
Figure 7. Variation of short-time energies with time for different grades rockbursts
图 8 不同等级岩爆的弹射动能与局部声响总能量
Figure 8. Kinetic energy and total energy of local sound for different grades of rockbursts
表 1 3种典型破坏现象的声音信号波形特性
Table 1. Features of voice signal waveform for three typical failure phenomena
破坏现象 波形 持续时间/s NA > 0.4 颗粒弹射 “笋芽”状 0.03 1 岩板劈裂 “矛头”状 0.47 < 10 块片弹射 “三角”状 0.76 > 100 
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表 2 3种典型破坏现象的声音信号频谱
Table 2. Spectra of sound signals for three typical failure phenomena
破坏现象 频谱形状 主频值/kHz 归一化幅值 颗粒弹射 多峰 10.58 0.009 岩板劈裂 单峰 2.13 0.005 块片弹射 单峰 0.46 0.021
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表 3 3种典型破坏现象的声纹特性
Table 3. Voiceprints of sound signals for three typical failure phenomena
破坏现象 声纹体型 能量幅值 频率范围/kHz 颗粒弹射 “带”状 1.0~2.4 7~11 岩板劈裂 “鳞片”状 2.0~5.7 2~8 块片弹射 “条”状 4.0~18.0 0~2
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表 4 3种典型破坏现象的声音信号短时能量特性
Table 4. Short-time energy features of sound signals for three typical failure phenomena
破坏现象 形状 振荡频度 最大幅值 颗粒弹射 平滑曲线 无振荡 0.34 岩板劈裂 局部振荡 快速 2.30 块片弹射 连续振荡 急速 8.60
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表 5 基于RF模型的岩爆典型破坏现象识别结果
Table 5. Identification of typical failure phenomena using RF model
名称 训练样本数 训练样本识别率/% 预测样本数 预测样本识别准确率/% 颗粒弹射 124 95 22 88 岩板劈裂 52 93 10 91 块片弹射 54 96 9 90
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[1] 冯夏庭.岩爆孕育过程的机制、预警与动态调控[M].北京:科学出版社, 2013. [2] 高玮, 张飞君.深部地下工程岩爆预测的筛选蚁群聚类算法[J].爆炸与冲击, 2012, 32(6):568-572. doi: 10.11883/1001-1455(2012)06-0568-05GAO Wei, ZHANG Feijun. Forecasting of rockburst indeep underground engineering based on abstraction ant colony clustering algorithm[J]. Explosion and Shock Waves, 2012, 32(6):568-572. doi: 10.11883/1001-1455(2012)06-0568-05 [3] HE M C, MIAO J L, FENG J L. Rock burst process of limestone and its acoustic emission characteristics under true-triaxial unloading conditions[J]. International Journal of Rock Mechanics and Mining Sciences, 2010, 47(2):286-298. doi: 10.1016/j.ijrmms.2009.09.003 [4] 陈炳瑞, 冯夏庭, 曾雄辉, 等.深埋隧洞TBM掘进微震实时监测与特征分析[J].岩石力学与工程学报, 2011, 30(2):275-283. http://industry.wanfangdata.com.cn/dl/Detail/Periodical?id=Periodical_yslxygcxb201102008CHEN Bingrui, FENG Xiating, ZENG Xionghui, et al. Real-time microseismic monitoring and its characteristic analysis during TBM tunneling in deep-buried tunnel[J]. Chinese Journal of Rock Mechanics and Engineering, 2011, 30(2):275-283. http://industry.wanfangdata.com.cn/dl/Detail/Periodical?id=Periodical_yslxygcxb201102008 [5] 中华人民共和国国家标准编写组. GB 50287—2006水力发电工程地质勘察规范[S]. 北京: 中国计划出版社, 2006. [6] 刘正雄.岩爆预防及防治技术研究[J].中国铁道科学, 2001, 22(4):74-76. http://www.cqvip.com/QK/97928X/200104/5369676.htmlLIU Zhengxiong. Technical study on the prevention and cure of rockburst[J]. China Railway Science, 2001, 22(4):74-76. http://www.cqvip.com/QK/97928X/200104/5369676.html [7] 周辉, 孟凡震, 张传庆, 等.深埋硬岩隧洞岩爆的结构面作用机制分析[J].岩石力学与工程学报, 2015, 34(4):720-727. http://www.cnki.com.cn/Article/CJFDTOTAL-YSLX201504008.htmZHOU Hui, MENG Fanzhen, ZHANG Chuanqing, et al. Analysis of the structural plane controlling mechanism on rockburst in deep hard rock tunnels[J]. Chinese Journal of Rock Mechanics and Engineering, 2015, 34(4):720-727. http://www.cnki.com.cn/Article/CJFDTOTAL-YSLX201504008.htm [8] 何满潮, 苗金丽, 李德建, 等.深部花岗岩试样岩爆过程实验研究[J].岩石力学与工程学报, 2007, 26(5):865-876. http://www.cqvip.com/qk/96026X/200705/25633809.htmlHE Manchao, MIAO Jinli, LI Dejian, et al. Experimental study on rockburst processes of granite specimen at great depth[J]. Chinese Journal of Rock Mechanics and Engineering, 2007, 26(5):865-876. http://www.cqvip.com/qk/96026X/200705/25633809.html [9] 冯夏庭, 陈炳瑞, 明华军, 等.深埋隧洞岩爆孕育规律与机制:即时型岩爆[J].岩石力学与工程学报, 2012, 31(3):433-444. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yslxygcxb201203001FENG Xiating, CHEN Bingrui, MING Huajun, et al. Evolution law and mechanism of rockbursts in deep tunnels: immediate rockburst[J]. Chinese Journal of Rock Mechanics and Engineering, 2012, 31(3):433-444. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yslxygcxb201203001 [10] SU Guoshao, JIANG Jianqing, ZHAI Shaobin, et al. Influence of tunnel axis stress on strainburst: an experimental study[J]. Rock Mechanics and Rock Engineering, 2017, 50(6):1551-1567. doi: 10.1007/s00603-017-1181-7 [11] 宫凤强, 罗勇, 司雪峰, 等.深部圆形隧洞板裂屈曲岩爆的模拟试验研究[J].岩石力学与工程学报, 2017, 36(7):1634-1648. http://www.doc88.com/p-9137835519775.htmlGONG Fengqiang, LUO Yong, SI Xuefeng, et al. Experimental modelling on rockburst in deep hard rock circular tunnels[J]. Chinese Journal of Rock Mechanics and Engineering, 2017, 36(7):1634-1648. http://www.doc88.com/p-9137835519775.html [12] 苏国韶, 蒋剑青, 冯夏庭, 等.岩爆弹射破坏过程的试验研究[J].岩石力学与工程学报, 2016, 35(10):1990-1999. http://www.wanfangdata.com.cn/details/detail.do?_type=degree&id=Y2888927SU Guoshao, JIANG Jianqing, FENG Xiating, et al. Experimental study on ejection process in rockburst[J]. Chinese Journal of Rock Mechanics and Engineering, 2016, 35(10):1990-1999. http://www.wanfangdata.com.cn/details/detail.do?_type=degree&id=Y2888927 [13] 王小川.MATLAB神经网络43个案例分析[M].北京:北京航空航天大学出版社, 2013. [14] 李宏松, 苏健民, 黄英来, 等.基于声音信号的特征提取方法的研究[J].信息技术, 2006, 30(1):91-94. http://mall.cnki.net/magazine/Article/HDZJ200601027.htmLI Hongsong, SU Jianmin, HUANG Yinglai, et al. The research on characteristics extraction based on voice signal[J]. Information Technology, 2006, 30(1):91-94. http://mall.cnki.net/magazine/Article/HDZJ200601027.htm [15] GIANNAKOPOULOS T, PIKRAKIS A. Introduction to audio analysis[M]. Amsterdam: Elsevier Academic Press, 2014. [16] 刘庆升, 徐霄鹏, 黄文浩.一种语音端点检测方法的探究[J].计算机工程, 2003, 29(3):120-121. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jsjgc200303047LIU Qingsheng, XU Xiaopeng, HUANG Wenhao. Research on a speech endpoint detection method[J]. Computer Engineering, 2003, 29(3):120-121. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jsjgc200303047 [17] BREIMAN L. Random forests[J]. Machine Learning, 2001, 45(1):5-32. https://en.wikipedia.org/wiki/Random_forest [18] DONG Longjun, LI Xibing, PENG Kang. Prediction of rockburst classification using random forest[J]. Transactions of Nonferrous Metals Society of China, 2013, 23(2):472-477. doi: 10.1016/S1003-6326(13)62487-5 [19] CUTLER A, CUTLER D R, STEVENS J R. Random forests[M]//ZHANG Cha, MA Yunqian. Ensemble Machine Learning. US: Springer Publishing, 2012: 157-175. -
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