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基于EMD_SVD的矿山微震与爆破信号特征提取及分类方法

尚雪义, 李夕兵, 彭康, 董陇军, 王泽伟

尚雪义, 李夕兵, 彭康, 董陇军, 王泽伟. 基于EMD_SVD的矿山微震与爆破信号特征提取及分类方法[J]. 岩土工程学报, 2016, 38(10): 1849-1858. DOI: 10.11779/CJGE201610014
引用本文: 尚雪义, 李夕兵, 彭康, 董陇军, 王泽伟. 基于EMD_SVD的矿山微震与爆破信号特征提取及分类方法[J]. 岩土工程学报, 2016, 38(10): 1849-1858. DOI: 10.11779/CJGE201610014
shu
SHANG Xue-yi, LI Xi-bing, PENG Kang, DONG Long-jun, WANG Ze-wei. Feature extraction and classification of mine microseism and blast based on EMD-SVD[J]. Chinese Journal of Geotechnical Engineering, 2016, 38(10): 1849-1858. DOI: 10.11779/CJGE201610014
Citation: SHANG Xue-yi, LI Xi-bing, PENG Kang, DONG Long-jun, WANG Ze-wei. Feature extraction and classification of mine microseism and blast based on EMD-SVD[J]. Chinese Journal of Geotechnical Engineering, 2016, 38(10): 1849-1858. DOI: 10.11779/CJGE201610014
shu

基于EMD_SVD的矿山微震与爆破信号特征提取及分类方法  English Version

  • 1. 中南大学资源与安全工程学院,湖南 长沙 410083；
    2. 重庆大学煤矿灾害动力学与控制国家重点实验室,重庆 400044

基金项目: 国家自然科学基金项目（41272304）; 国家重点研发项目（2016YFC0600706）; 国家自然科学基金青年基金项目（51504044）
详细信息
    作者简介:

    尚雪义(1989- ),男,博士研究生,主要从事矿山微震信号处理、微震区域性分析及震源机制等方面的研究工作。E-mail: shangxueyi@csu.edu.cn。

    通讯作者:

    李夕兵，E-mail：xbli@mail.csu.edu.cn

Feature extraction and classification of mine microseism and blast based on EMD-SVD

  • 1. School of Resources and Safety Engineering, Central South University, Changsha 410083, China;
    2. State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China

摘要

    摘要
  • 摘要: 针对矿山微震与爆破信号难以识别的问题,提出了基于经验模态分解（EMD）和奇异值分解（SVD）的矿山信号特征提取及分类方法。首先对微震与爆破信号进行EMD分解,再借助相关系数和方差贡献率筛选得到主要本征模态分量为IMF1~IMF6,进而利用SVD计算主要本征模态分量构成矩阵的奇异值σi(i=1,2···,6),最后应用支持向量机（SVM）对用沙坝矿微震与爆破信号进行分类。结果表明：微震与爆破信号的奇异值σ1,σ2和σ3差异较大,且σ1=7.5作为识别分界值时准确率达到了88.25%;SVM法识别效果优于BP神经网络法、Bayes法和单一奇异值分界值法,且SVM法准确率达到了93.0%。由此,该方法可为矿山微震与爆破信号特征提取和分类提供一种新方法。
    Abstract: To solve the difficult problem in identifying rock mass microseism and blasting vibration signals, a method for feature extraction and classification is proposed based on the empirical mode decomposition (EMD) and singular value decomposition (SVD). Firstly, the mine signals are decomposed by EMD, and the IMF1 to IMF6 selected by correlation coefficients and variance contribution ratios are the main intrinsic mode functions (IMFs). Then the SVD is used to obtain singular valuesσi(i=1,2···,6) of feature vector matrix constructed of the main IMFs. Furthermore, the support vector machine (SVM) is adopted to train, classify and recognize the signals of Yongshaba mine. The results show that there are large differences of singular valuesσ1,σ2 and σ3 between microseisms and blasts, and the best pattern recognition is obtained whenσ1 is 7.5 with an accuracy rate of 88.25%. In addition, the SVM method with an accuracy rate of 93% is better than the BP neural network method, Bayes method and boundary value method. In conclusion, the proposed method provides a new way for the feature extraction and classification of mine microseism and blast.
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  • 收稿日期:  2015-08-29
  • 发布日期:  2016-10-24

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