Abstract：Water inrush accidents threaten the lives of people and cause property damage. Therefore, it has great significance in accurately detecting the type of water inrush. Hydrochemical analysis method takes a long time and has a complicated process to detect the type of water inrush. Laser-induced fluorescence (LIF) technique has the advantages of fastness, high sensitivity, and low interference. Water inrush source recognition model building with LIF technique and intelligent algorithms can accurately detect the type of water inrush. At present, such models generally require de-noising, dimension reduction, and band selection on the fluorescence spectra, and this process is complicated. The models are built on the fluorescence spectra of the water inrush source which is evenly grouped. The influence of the uneven grouping on the model is not discussed, and the model is not built for the uneven grouping. In practical engineering applications, the number of samples collected is highly likely to be uneven, so Moth-flame optimization (MFO) algorithm combined with spectral clustering (SC) is proposed to realize the uneven grouping of water inrush fluorescence spectrain this paper. In the experiment, firstly, five kinds of experimental water samples were obtained from Huainan coal mine. Laser-induced fluorescence experimental equipment was used to collect fluorescence spectra of all water samples. The number of groups of five water samples is 75, 80, 80, 30 and 135. Secondly, build MFO-SC water sample recognition model. After comparison, K-Means is selected for the label mapping method, the Gaussian kernel function is selected for the calculation method of the similarity matrix, and the ncut is selected for the partition criterion. The parameters of the Gaussian kernel function were optimized by using MFO to obtain the parameter value of 1.745, and the initial clustering center of the model was fixed. Subsequently, build three water sample recognition models of K-Means, SVM and MFO-SVM, respectively. Comparing the MFO-SC model with the K-Means model, the optimal accuracy of the MFO-SC model is 100%, and the average accuracy is 100%. The optimal accuracy of the K-Means model is 99.75%, and the average accuracy is 79.57%. Then calculate the training set accuracy and test set accuracy of the SVM model and MFO-SVM model respectively. The accuracy of the training set of the SVM model is 80%, and the accuracy of the test set is 80%; the accuracy of the training set of the MFO-SVM model is 100%, and the accuracy of the test set is 95.625%. Finally, four models were used to identify water inrush fluorescence spectra of the other three uneven groups. The research results show that the MFO-SC algorithm is effective in identifying the type of water inrush, and can accurately detect the type of water inrush, which has great significance on the safety of coal mine production.
周孟然，宋红萍，胡 锋，来文豪，王锦国. 谱聚类结合LIF在矿井突水水源类型识别中的应用[J]. 光谱学与光谱分析, 2021, 41(02): 435-440.
ZHOU Meng-ran, SONG Hong-ping, HU Feng, LAI Wen-hao, WANG Jin-guo. Application of Spectral Clustering and LIF in Recognition of Mine Water Inrush Source Types. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(02): 435-440.
