Study on Coal-Rock Identification Method Based on Terahertz Time-Domain Spectroscopy

Coal-rock identification is one of the key problems restricting unmanned coal mining. Because of the extremely complicated working environment, the traditional manual coal mining is difficult to find the interface of coal and rock accurately, which is easy to cause the phenomenon of undercutting or overcutting. As a non-destructive detection method, Terahertz spectroscopy can reflect the physical and chemical information of the object under test and be an effective method to study the identification of coal and rock. In this paper, the terahertz time-domain spectroscopy and multivariate statistical method-cluster analysis (CA) and principal component analysis (PCA) are used to identify different types of coal and rock. The THz spectra of six coal and rock samples are obtained by transmission terahertz spectrometer. FFT and other mathematical calculations can obtain various samples' refractive index, absorption coefficient and dielectric constant. The results show differences in the refractive index and absorption coefficient of different types of coal and rock. By analyzing the relationship between the refractive index and absorption coefficient of various coal samples and the content of each component of the samples, it can be found that carbon content is one of the factors affecting the refractive index of the samples, and ash content is one of the factors affecting the absorption coefficient of the samples. The Euclidean distance of two kinds of samples in cluster analysis and the score of PC1 in principal component analysis can reflect the similarity and dissimilarity between coal and rock samples, and the results of CA and PCA are consistent. The refractive index and absorption coefficient of various samples in the 0. 5 similar to 2. 5 THz frequency range are combined with CA and PCA to form a model between terahertz data and coal and rock. According to the analysis, the six types of coal samples in the two models can be divided into two types based on the similarity between different samples. In the CA-PCA model with the absorption coefficient of various samples adopted, four kinds of coal are clustered together. Moreover, quartz sandstone (GSR-4) has a unique characteristic ; quartz sandstone has the smallest PC1 score value, and the Euclidian distance between quartz sandstone and the second type is the largest, up to 219. 03. It can be seen that the combination of terahertz technology and multivariate statistical method can realize the accurate identification of coal and rock, and the recognition accuracy can reach 100%.