【摘要】：針對(duì)巖體破裂信號(hào)與爆破振動(dòng)信號(hào)難以自動(dòng)識(shí)別的問(wèn)題,提出了基于局部均值分解(LMD)和模式識(shí)別的礦山微震信號(hào)特征提取及分類(lèi)方法。首先采用LMD對(duì)微震信號(hào)進(jìn)行自適應(yīng)分解得到乘積函數(shù)(PF)分量,再利用相關(guān)系數(shù)和方差貢獻(xiàn)率篩選得到PF主分量,進(jìn)而計(jì)算各主分量的相關(guān)系數(shù)和能譜系數(shù),并以此作為模式識(shí)別的特征向量。結(jié)果表明:LMD、經(jīng)驗(yàn)?zāi)B(tài)分解(EMD)和離散小波變化(DWT)的主分量分別為PF1~PF6,IMF1~IMF6和D2~D7,其中IMFi(i=1,2,…,6)為EMD分解的本征模態(tài)分量,Dj(j=2,3,…,7)為DWT分解的細(xì)節(jié)分量;LMD主分量分類(lèi)識(shí)別結(jié)果整體上優(yōu)于EMD和DWT主分量分類(lèi)識(shí)別結(jié)果;能譜系數(shù)分類(lèi)結(jié)果整體上優(yōu)于相關(guān)系數(shù)分類(lèi)結(jié)果,人工神經(jīng)網(wǎng)絡(luò)(ANN)和支持向量機(jī)(SVM)識(shí)別效果明顯優(yōu)于邏輯回歸(LR)和Bayes判別法識(shí)別結(jié)果,且基于LMD能譜系數(shù)的SVM分類(lèi)準(zhǔn)確率達(dá)到了93.0%。
[Abstract]:In view of the problem that the rock burst signal and the blasting vibration signal are difficult to recognize automatically, a method based on local mean mean decomposition (LMD) and pattern recognition is proposed to extract and classify the characteristics of the micro earthquake signals. First, the product function (PF) component is obtained by LMD adaptive decomposition of the microseismic signal, and the correlation coefficient and the variance contribution rate sieve are used. The principal components of PF are selected and then the correlation coefficients and the spectral coefficients of the main components are calculated and used as the eigenvectors of the pattern recognition. The results show that the main components of the LMD, the empirical mode decomposition (EMD) and the discrete wavelet change (DWT) are PF1~PF6, IMF1~IMF6 and D2~D7 respectively, and IMFi (i=1,2,...) 6) the eigenmode component of the EMD decomposition, Dj (j=2,3,...) 7) is the detail component of DWT decomposition; the LMD principal component classification recognition results are better than the EMD and DWT principal component classification recognition results. The classification results of the energy spectrum coefficient are better than the correlation coefficient classification results as a whole. The recognition results of the artificial neural network (ANN) and the support vector machine (SVM) are obviously better than the logical regression (LR) and Bayes discriminant recognition results. The SVM classification accuracy of LMD spectrum coefficient reached 93.0%.
【作者單位】： 黑龍江科技大學(xué)黑龍江省普通高校采礦工程重點(diǎn)實(shí)驗(yàn)室;黑龍江科技大學(xué)礦業(yè)工程學(xué)院;
【基金】：黑龍江省普通高等學(xué)校采礦工程重點(diǎn)實(shí)驗(yàn)室開(kāi)放課題資助項(xiàng)目(2014KF04) 黑龍江省自然科學(xué)基金面上資助項(xiàng)目(E2016061)
【分類(lèi)號(hào)】：TD311;TN911.7

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