FSWT_SVD模型在岩体微震信号特征提取中的应用

尚雪义1 李夕兵1 彭 康2,3 王泽伟1 翁 磊1

振动与冲击 ›› 2017, Vol. 36 ›› Issue (14) : 52-60.

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振动与冲击 ›› 2017, Vol. 36 ›› Issue (14) : 52-60.
论文

FSWT_SVD模型在岩体微震信号特征提取中的应用

  • 尚雪义1   李夕兵1   彭  康2,3   王泽伟1   翁  磊1
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Application of FSWT_SVD Model for Feature Extraction of Rock Mass Microseismic Signal

  • SHANG Xueyi1   LI Xibing1   PENG Kang2,3   WANG Zewei1   WENG Lei1
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摘要

针对岩体破裂信号与爆破振动信号难以识别的问题,本文采用傅里叶变换(FT)得到其频谱分布,并确定划分为6个频带提取信号特征。借助频率切片小波变换(FSWT)将信号按上述频带切片并重构,再利用奇异值分解(SVD)得到上述频带重构信号所组成矩阵的奇异值 ,从而实现岩体微震信号的特征提取。进而对用沙坝矿120个岩体破裂和120个爆破振动信号展开FSWT_SVD分析,最后利用BP神经网络对奇异值矩阵进行分类训练和预测。结果表明:(1) 岩体破裂信号与爆破振动信号的奇异值 相差最大, 、 、 和 相差较大,而 差异不明显,且当 作为单一奇异值法识别分界值时效果最优,准确率达到了86.67%;(2) BP神经网络法分类识别结果较LR法、Bayes法和Fisher法优,SVD提取特征识别效果较能量比和相关系数优,FSWT重构矩阵提取的特征信息优于DWT重构矩阵提取的特征信息,且基于FSWT_SVD的BP法分类识别准确率达到了91%。综上知,基于FSWT_SVD的BP神经网络模型为岩体破裂与爆破信号特征提取和模式识别提供了一种新方法。

Abstract

To solve the problem that it is difficult to identify rock mass fracturing signals and blasting vibration signals, the Fourier transform (FT) was applied to gain their spectrum distributions and six frequency bands were needed for the feature extraction analysis. Then the rock mass fracturing signals were divided and reconstructed by frequency slice wavelet transform (FSWT), and the singular value decomposition (SVD) was employed to obtain singular values   of matrixes composed of the six reconstructed components, thus the feature extraction of rock mass fracturing signals was implemented. Furthermore, 120 sets of rock mass fracturing signals and 120 sets of blasting vibration signals obtained from Yongshaba mine were analysed using the FSWT_SVD method. Finally, the back propagation (BP) neural network was adopted to train, classify and recognize the eigenvectors obtained from the singular values. The results show that: (1) There are large differences of  , comparatively large differences of  ,  ,   and  , and little differences of   between rock mass fracturing signals and blasting vibration signals, and the best pattern recognition was obtained when   is eight with an accuracy rate of 86.67%. (2) The classification result based on BP neural network is the best among the BP, Logistic Regression (LR), Bayes and Fisher based methods, the feature matrix obtained by the SVD is better than that obtained by the energy ratio and correlation coefficient matrix, and the feature matrix obtained from FSWT is better than that of discrete wavelet transform (DWT). Furthermore, the BP classifier based on FSWT_SVD achieves a correct identification rate of 91%. In conclusion, the BP classifier based on FSWT_SVD provides a new way for feature extraction and pattern recognition of rock mass fracturing signal and blasting vibration signal.
 

关键词

岩体微震信号 / 频率切片小波变换 / 奇异值分解 / 特征提取 / 模式识别

Key words

  / Rock mass microseismic signal;frequency slice wavelet transform(FSWT);singular value decomposition(SVD);feature extraction;pattern recognition

引用本文

导出引用
尚雪义1 李夕兵1 彭 康2,3 王泽伟1 翁 磊1. FSWT_SVD模型在岩体微震信号特征提取中的应用[J]. 振动与冲击, 2017, 36(14): 52-60
SHANG Xueyi1 LI Xibing1 PENG Kang2,3 WANG Zewei1 WENG Lei1. Application of FSWT_SVD Model for Feature Extraction of Rock Mass Microseismic Signal[J]. Journal of Vibration and Shock, 2017, 36(14): 52-60

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