Research on Mine Water Inrush Identification Based on LIF and
LSTM Neural Network
YAN Peng-cheng1, 2, ZHANG Xiao-fei2*, SHANG Song-hang2, ZHANG Chao-yin2
1. State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mine, Anhui University of Science and Technology, Huainan 232001, China
2. College of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China
Abstract：Mine water disasteris a great threat to the safety production of a coal mine, so the rapid identification of mine water inrush source is of great significance to the early warning and post-disaster rescue work. Laser-induced fluorescence (LIF) technology has high speed, high efficiency and high sensitivity, which overcomes the shortcomings of long recognition time in traditional hydrochemical methods. Circulating neural network (RNN) has obvious shortcomings in solving the problems of gradient disappearance and gradient explosion in long sequence training, while the special variant RNN, long and short term memory (LSTM) neural network, makes up for the shortcomings of RNN.In this paper, the combination of LIF technology and LSTM algorithm is applied to rapidly identify mine water inrush source.The experimental samples were collected from Huainan Mining Area. The sandstone water and goaf water were taken as the original samples, and the sandstone water and goaf water were mixed into 5 kinds of mixed water samples. According to different proportions, 7 kinds of water samples experimented. Firstly, MinMaxSxalerr, SG and SNV were used to preprocess the original spectral data to reduce the noise and interference. After that, to prevent the data from being too large and too high a dimension, the dimension of four groups of data, including the original spectral data, was reduced to 3 dimensions by LDA.Finally, the LSTM recognition models are built respectively, and the optimal model is selected by comparing the prediction accuracy of the test set, the changing trend of the accuracy and the loss function of the training set.Thereinto, SG+LDA+LSTM and Original+LDA+LSTM can reach 100% in the test set prediction accuracy, MinMaxScaler+LDA+LSTM test set prediction accuracy is 98.57%, SNV+LDA+LSTM accuracy is the lowest, only 87.14%;In terms of the trend of training set accuracy, SG+LDA+LSTM can keep good learning and reach 100% soon. Original+LDA+LSTM and MinMaxScaler+LDA+LSTM can also reach 100% accuracy. However, at the beginning of the training process, the accuracy will decline, and the SNV+LDA+LSTM training set does not reach 100% within the training times; The trend of SG+LDA+LSTM loss function also has good convergence and stability. Original+LDA+LSTM, MinMaxScalerr+LDA+LSTM and SNV+LDA+LSTM do not perform well in the trend of loss function.The results show that the SG+LDA+LSTM model is the most suitable for mine water inrush identification among the four models. This method supplements the work of mine water inrush source identification and provides a new idea for mine water inrush identification.
闫鹏程，张孝飞，尚松行，张超银. LIF结合LSTM神经网络的矿井水源识别[J]. 光谱学与光谱分析, 2022, 42(10): 3091-3096.
YAN Peng-cheng, ZHANG Xiao-fei, SHANG Song-hang, ZHANG Chao-yin. Research on Mine Water Inrush Identification Based on LIF and
LSTM Neural Network. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3091-3096.
