|
|
|
|
|
|
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.
|
Received: 2021-08-04
Accepted: 2021-11-05
|
|
Corresponding Authors:
ZHANG Xiao-fei
E-mail: 1285104634@qq.com
|
|
[1] WANG Xian-zheng(王显政). Safety in Coal Mines(煤矿安全), 2020, 51(10):1.
[2] YUAN Liang, ZHANG Ping-song(袁 亮, 张平松). Journal of China Coal Society(煤炭学报), 2019, 44(8):2277.
[3] WU Qiang, XU Hua, ZHAO Ying-wang, et al(武 强, 徐 华, 赵颖旺, 等). Journal of China Coal Society(煤炭学报), 2018, 43(10):2661.
[4] Hu Feng, Zhou Mengran, Yan Pengcheng, et al. IEEE Access, 2019, 7:107129.
[5] LIU Shou-qiang, WU Qiang, ZENG Yi-fan(刘守强, 武 强, 曾一凡). Coal Engineering(煤炭工程), 2019, 51(3):1.
[6] SUN Ji-ping, JIN Chun-hai(孙继平, 靳春海). Industry and Mine Automation(工矿自动化), 2019, 45(4):1.
[7] WANG Tian-tian, JIN De-wu, LIU Ji, et al(王甜甜, 靳德武, 刘 基, 等). Journal of China Coal Society(煤炭学报), 2019, 44(9):2840.
[8] LU Xin-pei, WU Fan, LI Jia-yin(卢新培, 吴 帆, 李嘉胤). High Voltage Engineering(高电压技术), 2021, 47(5): 1831.
[9] CHEN Zhi-kun, GUO Rui, CHENG Peng-fei(陈至坤, 郭 蕊, 程朋飞). Laser & Optoelectronics Progress(激光与光电子学进展), 2020, 57(13):332.
[10] ZHANG Da-yuan, LI Bo, GAO Qiang, et al(张大源, 李 博, 高 强, 等). Journal of Combustion Science and Technology(燃烧科学与技术), 2019, 25(2): 112.
[11] ZHU Jia-jian, WAN Ming-gang, WU Ge, et al(朱家健, 万明罡, 吴 戈, 等). Chinese Journal of Lasers(中国激光), 2021, 48(4): 78.
[12] YAN Peng-cheng, SHANG Song-hang, ZHOU Meng-ran, et al(闫鹏程, 尚松行, 周孟然, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2020, 40(7): 2176.
[13] LIU Li-jun, LEI Yu, YU Zhen(刘利军, 雷 宇, 余 臻). Aerospace Control(航天控制), 2020, 38(5): 67.
|
[1] |
XU Lu1, CHEN Yi-yun1, 2, 3*, HONG Yong-sheng1, WEI Yu1, GUO Long4, Marc Linderman5. Estimation of Soil Organic Carbon Content by Imaging Spectroscopy With Soil Roughness[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(09): 2788-2794. |
[2] |
YANG Jie-kai1, GUO Zhi-qiang1, HUANG Yuan2, 3*, GAO Hong-sheng1, JIN Ke1, WU Xiang-shuai2, YANG Jie1. Early Classification and Detection of Melon Graft Healing State Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(07): 2218-2224. |
[3] |
HE Nian, SHAN Peng*, HE Zhong-hai, WANG Qiao-yun, LI Zhi-gang, WU Zhui. Study on the Fractional Baseline Correction Method of ATR-FTIR
Spectral Signal in the Fermentation Process of Sodium Glutamate[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1848-1854. |
[4] |
YAN Peng-cheng1, 2, ZHANG Chao-yin2*, SUN Quan-sheng2, SHANG Song-hang2, YIN Ni-ni1, ZHANG Xiao-fei2. LIF Technology and ELM Algorithm Power Transformer Fault Diagnosis Research[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1459-1464. |
[5] |
WANG Fang-fang1, ZHANG Xiao-dong1, 2*, PING Xiao-duo1, ZHANG Shuo1, LIU Xiao1, 2. Effect of Acidification Pretreatment on the Composition and Structure of Soluble Organic Matter in Coking Coal[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 896-903. |
[6] |
ZHANG Fu1, 2, 3, CUI Xia-hua1, DING Ke4*, ZHANG Ya-kun1, WANG Yong-xian1, PAN Xiao-qing5. Study on the Influence of Different Pretreatment Methods on Gender Determination of Multiposition[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 434-439. |
[7] |
CHEN Fu-shan1, WANG Gao-min1, WU Yue1, LU Peng2, JI Zhe1, 2*. Advances in the Application of Confocal Raman Spectroscopy in Lignocellulosic Cell Walls Pretreatment[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 15-19. |
[8] |
MAO Ya-chun1, WEN Jian1*, FU Yan-hua2, CAO Wang1, ZHAO Zhan-guo3, DING Rui-bo1. Quantitative Inversion Model Based on the Visible and Near-Infrared Spectrum for Skarn-Type Iron Ore[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 68-73. |
[9] |
SUN Dai-qing1, 2, XIE Li-rong1*, ZHOU Yan2, GUO Yu-tao1, CHE Shao-min2. Application of SG-MSC-MC-UVE-PLS Algorithm in Whole Blood Hemoglobin Concentration Detection Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(09): 2754-2758. |
[10] |
FU Juan-juan, MA Dan-ying, TANG Jin-lan, BAO Yi-lin, ZHAO Yuan, SHANG Lin-wei, YIN Jian-hua*. NIR Spectroscopic Study and Staging Diagnosis of Osteoarthritic Articular Cartilage[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(09): 2770-2775. |
[11] |
LIU Yu1, LI Zeng-wei2, DENG Zhi-peng1, ZHANG Qing-xian1*, ZOU Li-kou2*. Fast Detection of Foodborne Pathogenic Bacteria by Laser-Induced Fluorescence Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(09): 2817-2822. |
[12] |
YAN Peng-cheng1, 2, SHANG Song-hang2*, ZHANG Chao-yin2, ZHANG Xiao-fei2. Classification of Coal Mine Water Sources by Improved BP Neural Network Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(07): 2288-2293. |
[13] |
XU Bo1, XU Tong-yu1, 2*, YU Feng-hua1, 2, ZHANG Guo-sheng1, FENG Shuai1, GUO Zhong-hui1, ZHOU Chang-xian1. Inversion Method for Cellulose Content of Rice Stem in Northeast Cold Region Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(06): 1775-1781. |
[14] |
LI Qing-bo1, BI Zhi-qi1, SHI Dong-dong2. The Method of Fishmeal Origin Tracing Based on EDXRF Spectrometry Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(03): 745-749. |
[15] |
LIU Hong-ming1,2, LIU Yu-juan1*, ZHONG Zhi-cheng1, SONG Ying1*, LI Zhe1, XU Yang1. Detection and Analysis of Water Content of Crude Oil by Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(02): 505-510. |
|
|
|
|