光谱学与光谱分析 |
|
|
|
|
|
Research on the Source Identification of Mine Water Inrush Based on LIF Technology and SIMCA Algorithm |
YAN Peng-cheng1, ZHOU Meng-ran1*, LIU Qi-meng2, 3, ZHANG Kai-yuan1, HE Chen-yang1 |
1. College of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China 2. Anhui Provincial Key Lab of Geohazards Prevention and Environment Protection, Huainan 232001, China 3. College of Earth and Environment, Anhui University of Science and Technology, Huainan 232001, China |
|
|
Abstract Rapid source identification of mine water inrush is of great significance for early warning and prevention in mine water hazard. According to the problem that traditional chemical methods to identify source takes a long time, put forward a method for rapid source identification of mine water inrush with laser induced fluorescence (LIF) technology and soft independent modeling of class analogy (SIMCA) algorithm. Laser induced fluorescence technology has the characteristics of fast analysis, high sensitivity and so on. With the laser assisted, fluorescence spectrums can be collected real-time by the fluorescence spectrometer. According to the fluorescence spectrums, the type of water samples can be identified. If the database is completed, it takes a few seconds for coal mine water source identification, so it is of great significance for early warning and post-disaster relief in coal mine water disaster. The experiment uses 405 nm laser emission laser into the 5 kinds of water inrush samples and get 100 groups of fluorescence spectrum, and then put all fluorescence spectrums into preprocessing. Use 15 group spectrums of each water inrush samples, a total of 75 group spectrums, as the prediction set, the rest of 25 groups spectrums as the test set. Using principal component analysis (PCA) to modeling the 5 kinds of water samples respectively, and then classify the water samples with SIMCA on the basis of the PCA model. It was found that the fluorescence spectrum are obvious different of different water inrush samples. The fluorescence spectrums after preprocessing of Gaussian-Filter, under the condition of the principal component number is 2 and the significant level α=5%, the accuracy of prediction set and testing set are all 100% with the SIMCA to classify the water inrush samples.
|
Received: 2014-11-20
Accepted: 2015-02-25
|
|
Corresponding Authors:
ZHOU Meng-ran
E-mail: mrzhou8521@163.com
|
|
[1] WU Qiang, GUAN En-tai(武 强, 管恩太). Journal of China Coal Society(煤炭学报), 2006, 31(4): 409. [2] GAO Jian-ning(高建宁). Safety In Coal Mines(煤矿安全), 2013, 44(3): 156. [3] ZHAO Shan-shan, SUN Jian-hua(赵珊珊, 孙建华). Safety In Coal Mines(煤矿安全), 2009, ?40(4): 127. [4] YANG Hai-jun, WANG Guang-cai(杨海军, 王广才). Coal Geology & Exploration(煤田地质与勘探), 2012, 40(3): 48. [5] QIAN Jia-zhong, PAN Jing, ZHAO Wei-dong(钱家忠, 潘 婧, 赵卫东). Systems Engineering-Theory & Practice(系统工程理论与实践), 2011, 31(12): 2425. [6] LIAN Hui-qing, LIU De-min, YIN Shang-xian(连会青, 刘德民, 尹尚先), Coal Engineering(煤炭工程), 2012, 8: 107. [7] YAO Jie, TONG Min-ming, LIU Tao(姚 洁, 童敏明, 刘 涛). Safety in Coal Mines(煤矿安全), 2013, 44(2): 29. [8] LU Jin-tao, LI Xi-bing, GONG Feng-qiang(鲁金涛, 李夕兵, 宫凤强). China Safety Science Journal(中国安全科学学报), 2012, 22(7): 109. [9] CHEN Hong-jiang, LI Xi-bing, LIU Ai-hua(陈红江, 李夕兵, 刘爱华). Journal of Central South University(Science and Technology)(中南大学学报·自然科学版), 2009, 40(4): 1114. [10] ZHANG Yang, MA Yun-dong, WU Hao(张 洋, 马云东, 吴 浩). Hydrogeology & Engineering Geology(水文地质工程地质), 2014, 41(4): 32. [11] WEN Ting-xin, ZHANG Bo, SHAO Liang-shan(温廷新, 张 波, 邵良杉). China Safety Science Journal(中国安全科学学报), 2014, 24(2): 100. [12] HUANG Yue-hua, FAN Lu, LI Juan(黄月华, 范 璐, 李 娟). Journal of Henan University of Technology(Natural Science Edition)(河南工业大学学报·自然科学版), 2009, 30(5): 13.
|
[1] |
WANG Cai-ling1,ZHANG Jing1,WANG Hong-wei2*, SONG Xiao-nan1, JI Tong3. A Hyperspectral Image Classification Model Based on Band Clustering and Multi-Scale Structure Feature Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 258-265. |
[2] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
[3] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
[4] |
FANG Zheng, WANG Han-bo. Measurement of Plastic Film Thickness Based on X-Ray Absorption
Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3461-3468. |
[5] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
[6] |
JIA Zong-chao1, WANG Zi-jian1, LI Xue-ying1, 2*, QIU Hui-min1, HOU Guang-li1, FAN Ping-ping1*. Marine Sediment Particle Size Classification Based on the Fusion of
Principal Component Analysis and Continuous Projection Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3075-3080. |
[7] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
[8] |
XUE Fang-jia, YU Jie*, YIN Hang, XIA Qi-yu, SHI Jie-gen, HOU Di-bo, HUANG Ping-jie, ZHANG Guang-xin. A Time Series Double Threshold Method for Pollution Events Detection in Drinking Water Using Three-Dimensional Fluorescence Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3081-3088. |
[9] |
JIA Hao1, 3, 4, ZHANG Wei-fang1, 3, LEI Jing-wei1, 3*, LI Ying-ying1, 3, YANG Chun-jing2, 3*, XIE Cai-xia1, 3, GONG Hai-yan1, 3, DING Xin-yu1, YAO Tian-yi1. Study on Infrared Fingerprint of the Classical Famous
Prescription Yiguanjian[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3202-3210. |
[10] |
CAO Qian, MA Xiang-cai, BAI Chun-yan, SU Na, CUI Qing-bin. Research on Multispectral Dimension Reduction Method Based on Weight Function Composed of Spectral Color Difference[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2679-2686. |
[11] |
ZHANG Zi-hao1, GUO Fei3, 4, WU Kun-ze1, YANG Xin-yu2, XU Zhen1*. Performance Evaluation of the Deep Forest 2021 (DF21) Model in
Retrieving Soil Cadmium Concentration Using Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2638-2643. |
[12] |
CHEN Wan-jun1, XU Yuan-jie2, LU Zhi-yun3, QI Jin-hua3, WANG Yi-zhi1*. Discriminating Leaf Litters of Six Dominant Tree Species in the Mts. Ailaoshan Based on Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2119-2123. |
[13] |
WANG Yu-hao1, 2, LIU Jian-guo1, 2, XU Liang2*, DENG Ya-song2, SHEN Xian-chun2, SUN Yong-feng2, XU Han-yang2. Application of Principal Component Analysis in Processing of Time-Resolved Infrared Spectra of Greenhouse Gases[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2313-2318. |
[14] |
HU Hui-qiang1, WEI Yun-peng1, XU Hua-xing1, ZHANG Lei2, MAO Xiao-bo1*, ZHAO Yun-ping2*. Identification of the Age of Puerariae Thomsonii Radix Based on Hyperspectral Imaging and Principal Component Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1953-1960. |
[15] |
LIU Yu-juan1, 2, 3 , LIU Yan-da1, 2, 3, SONG Ying1, 2, 3*, ZHU Yang1, 2, 3, MENG Zhao-ling1, 2, 3. Near Infrared Spectroscopic Quantitative Detection and Analysis Method of Methanol Gasoline[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1489-1494. |
|
|
|
|