基于可见光-近红外光谱的煤岩识别方法实验研究

以淮南矿区谢桥矿和潘二矿的煤和岩石样本为研究对象，通过地物光谱仪采集样本反射率光谱曲线，同时检测样本氧化物含量、水分、灰分及挥发分含量，将样本的反射率光谱曲线和样本成分含量分别作为自变量，样本类别“煤”和“岩石”两种矿物类型作为因变量，建立煤和岩石识别模型对煤和岩石进行二分类。该研究主要采用三种模型，分别为主成分分析结合支持向量机(PCA-SVM)、主成分分析结合BP神经网络(PCA-BP)模型和核主成分分析结合支持向量机(KPCA-SVM)模型。结果表明，基于可见光近红外光谱的三个模型中，核主成分分析结合支持向量机模型的识别精度最高，建模平均精度为95.5%，验证平均精度约为90.56%；基于样本成分的三个模型中，核主成分分析结合支持向量机模型的识别精度最高，建模平均精度为98.5%，验证平均精度约为95%。

Experimental Research on Coal-Rock Identification Method Based on Visible-Near Infrared Spectroscopy

Taking the coal and rock samples retrieved from the Huainan Xieqiao Mine and the Paner II Mine as the research object, the sample reflectance spectrum curve was collected by a ground spectrometer, and the sample’s oxide content, moisture, ash and volatile content were simultaneously detected to reflect the sample’s reflection. The rate spectral curve and the sample component content are used as independent variables, and the sample type is used as the dependent variable to establish a coal and rock identification model to classify coal and rock. This paper mainly adopts three models, which are principal component analysis combined with support vector machine (PCA-SVM), principal component analysis combined with BP neural network (PCA-BP) model and kernel principal component analysis combined with support vector machine (KPCA-SVM) model. The results show that among the three models based on visible light near-infrared spectroscopy, nuclear principal component analysis combined with support vector machine model has the highest recognition accuracy, the average accuracy of modeling is 95.5%, and the average accuracy of verification is about 90.56%; three based on sample components. In the model, the kernel principal component analysis combined with the support vector machine model has the highest recognition accuracy, the average accuracy of modeling is 98.5%, and the average accuracy of verification is about 95%.