| 基于声音信号的室内岩爆动态预测方法 |
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| 引用本文： | 刘鑫锦,苏国韶,冯夏庭,燕柳斌,闫召富,张洁,李燕芳.基于声音信号的室内岩爆动态预测方法[J].岩土力学,2018,39(10):3573-3580. |
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| 作者姓名： | 刘鑫锦 苏国韶 冯夏庭 燕柳斌 闫召富 张洁 李燕芳 |
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| 作者单位： | 1. 广西大学 土木建筑工程学院，广西 南宁 530004；2. 广西大学 工程防灾与结构安全教育部重点实验室，广西 南宁 530004； 3. 东北大学 深部金属矿山安全开采教育部重点实验室，辽宁 沈阳 110819 |
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| 基金项目： | 国家自然科学基金(No. 51869003)；广西自然科学基金创新研究团队项目(No. 2016GXNSFGA380008)。 |
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| 摘 要： | 利用自主研发的真三轴岩爆试验机，在室内再现了应变型岩爆过程，并对岩爆过程中的声音信号进行监测。采用梅尔倒谱系数、谱质心、短时平均过零率等可定量化描述声音特性的组合指标作为岩爆过程典型破坏现象声音信号的特征提取信息，在此基础上结合适用于处理小样本、非线性分类问题的高斯过程机器学习方法，建立岩爆过程典型破坏现象识别的高斯过程模型，由此实现室内岩爆过程典型破坏现象的智能识别。进而，针对岩爆传统预测方法侧重于趋势预测而不能判别岩爆过程发展阶段的不足，采用智能识别+趋势预测的动态识别策略，以岩爆发生前夕的平静期、谐波均值、色谱向量均值等声音特征指标的变化规律作为岩爆前兆信息，提出一种多层次递进式的岩爆动态预测方法。室内岩爆的预测结果表明，该方法是可行的，为未来建立基于声音的现场岩爆预测方法奠定试验基础。
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| 关 键 词： | 岩石力学 岩爆 岩爆预测 声音信号 |
| 收稿时间： | 2017-04-12 |
Dynamic prediction method of laboratory rockburst using sound signals |
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| LIU Xin-jin,SU Guo-shao,FENG Xia-ting,YAN Liu-bin,YAN Zhao-fu,ZHANG Jie,LI Yan-fang.Dynamic prediction method of laboratory rockburst using sound signals[J].Rock and Soil Mechanics,2018,39(10):3573-3580. |
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| Authors: | LIU Xin-jin SU Guo-shao FENG Xia-ting YAN Liu-bin YAN Zhao-fu ZHANG Jie LI Yan-fang |
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| Institution: | 1. School of Civil and Architecture Engineering, Guangxi University, Nanning, Guangxi 530004, China; 2. Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, Guangxi University, Nanning, Guangxi 530004, China; 3. Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, Northeastern University, Shenyang, Liaoning 110819, China |
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| Abstract: | By using the self-developed true-triaxial rockburst testing machine, the rockburst processes were reproduced in laboratory and the sound signals of rockburst process were monitored. The combination index of Meyer cepstral coefficient, spectral centroid and short-time average zero-crossing rate, which can quantitatively describe the sound characteristics, was used as the feature extraction information of typical destructive phenomenon of rockburst process. Then, Gaussian process, a machine learning method for solving small sample, nonlinear classification problems, was used to construct an intelligent identification model. Thus, the intelligent identification of typical failure phenomena in a rockburst process was realized. In addition, in order to overcome the shortage of traditional rock burst prediction methods, which emphasize on trend prediction but can not distinguish the development stage of rock burst process, a multilevel, progressive and dynamic prediction method of laboratory rockburst was developed based on the strategy of intelligent recognition + trend prediction. The variation laws of acoustic characteristic indexes such as quiet period, harmonic mean value and chromatographic vector mean value before rockburst were taken as the precursor information of rock burst. The prediction results of different laboratory rockbursts indicate that the method is feasible and lays the testing foundation of the sound-based method for in situ rockburst prediction in the further. |
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| Keywords: | rock mechanics rockburst rockburst prediction sound signals |
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