基于半监督过采样非平衡学习的矿山微震信号识别

Mine microseismic signal recognition based on semi-supervised over-sampling imbalanced learning

为准确实现冲击矿压灾害的预防预警,提出一种半监督过采样框架对煤矿微震数据进行模式识别,采用主成分分析、小波变换和Fisher判别对微震数据集样本的多个信号通道进行特征提取;并对提取到的特征数据进行半监督非平衡学习;最后训练分类器进行模式识别。通过在兖矿集团微震数据集进行试验,结果表明:针对微震数据的半监督过采样框架可以有效提高微震数据的识别准确率。与只进行过采样的方法相比,使用CPLE和SELF两种半监督学习的方法,在KNN、LR、FLD、RF、SVM和Adaboost这6个分类器上有5个分类器上识别效果更好,可以获得更好的回归率和F1的指标。此方法可以获得高维微震数据的压缩表达,解决不平衡微震数据集的识别问题。

In order to improve the recognition accuracy of rock burst disaster,a semi-supervised over-sampling framework is proposed to recognize the microseismic signal. Principal component analysis（ PCA）,Wavelet transform and Fisher linear discriminant（ FLD） are used to extract features of the multi-channel data set. Then,over-sampling method and semi-supervised learning method are used to construct a balanced data set based on the features. Furthermore,six classifiers are trained based on the balanced microseismic data set,and the original data set is from Yancon Group Company. Experimental results show that the semi-supervised over-sampling framework is actually able to improve the classification accuracy. Compared with the method which only uses over-sampling algorithm,the semi-supervised oversampling framework can obtain higher values of recall and F1 score on 5 classifiers,with CPLE or SELF as the semi-supervised method.The six classifiers are KNN,LR,FLD,RF,SVM and Adaboost. Overall,this framework not only extracts the compressed representation of the high-dimensional data set,but also deals with the microseismic signal recognition problem based on a balanced data set.