Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (4): 1200-1210.doi: 10.13229/j.cnki.jdxbgxb.20220051

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Prediction of water⁃flowing height in fractured zone based on distributed optical fiber and multi⁃attribute fusion

Wen-li JI1,2(),Zhong TIAN1,Jing CHAI2,3(),Ding-ding ZHANG2,3,Bin WANG1   

  1. 1.College School of Communication and Information Engineering，Xi'an University of Science and Technology，Xi'an 710054，China
    2.Key Laboratory of Western Mine Exploitation and Hazard Prevention，Ministry of Education，Xi'an University of Science and Technology，Xi'an 710054，China
    3.College of Energy Engineering，Xi'an University of Science and Technology，Xi'an 710054，China
  • Received:2022-01-12 Online:2023-04-01 Published:2023-04-20
  • Contact: Jing CHAI E-mail:jiwenli@xust.edu.cn;chaij@xust.edu.cn

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Abstract:

The prediction height of water-flowing in fractured zone of overburden strata is the difficulty of research. A deep learning model is proposed to estimate the height of the water-conducting fracture zone based on simulation experiments of fiber-optic monitoring overlying rock deformation. The key factors， such as the frequency shift value of the optical fiber sensor， the height of the sensor， the advance length of the working face， the lithology and structure of the overlying rock were select in this model. Firstly， the frequency shift value is decomposed into three components， random frequency shift value， periodic frequency shift value and trend frequency shift value. Then， deep learning model are built to predict each component， and prediction component are superimposed to form the frequency shift value of sensor. Finally， the development height of the water-conducting fracture zone is inferred in accordance with the relationship between curve of frequency shift value change and the development height of fracture zone. The experiments show that the average estimation error of such height is about 11 mm. The predicted heights are accurate and reliable， which can also meet requirements of an engineering test practice.

Key words: smart mining, ensemble empirical mode decomposition, hybrid deep learning network, height of water-flowing fractured zone, prediction of frequency shift value of optical fiber sensor

CLC Number: 

  • TD325

Fig.1

Physical simulation test of similar materials based on distributed optical fiber monitoring"

Fig..2

Relation between curve of Fv1 fiber frequencyshift value and height of water?conducting zonein physical simulation test of similar materials"

Fig.3

Prediction framework of frequency shift value on vertical optical fiber full sensor"

Fig.4

EEMD decomposition of frequency shift valueon vertical fibers during working face goingto 40 cm"

Table 1

Hyperparameter setting of hybrid deep neuralnetwork"

模型参数IMF1设置IMF2设置RE设置
CNN的卷积核数161616
GRU层数及神经元2（48，56）2（87，72）2（30，71）
BP层数及神经元2（93，59）1（39）2（88，98）
输出神经单元数111
时间步长111
迭代轮次（Epochs）500500500
损失函数（Loss）MSEMSEMSE
批量大小（Batch_size）323232
优化算法AdamAdamAdam
初始学习率0.0010.0010.001

Fig.5

Prediction of frequency shift component aboutall sensor on vertical fiber Fv2"

Fig.6

Prediction of frequency shift about all sensor onvertical fiber Fv2"

Table 2

Comparison and analysis of evaluation indicators of different forecasting models"

评价指标组合模型Fv1光纤全测点预测Fv2光纤全测点预测
RMSE/MHzMAE/MHzR2RMSE/MHzMAE/MHzR2
GA?CNN?GRU99.163.00.52991.9963.30.565
FDT?GA?CNN?GRU?BP98.7259.950.63956.0238.450.714
EEMD?SVM37.831.30.93146.2239.860.88
EEMD?GA?CNN?GRU?BP4.943.00.9988.314.690.992
改进EEMD?GA?CNN?GRU?BP2.21.50.9984.72.70.997

Fig.7

Prediction of frequency shift about all sensor onvertical fiber Fv1"

Fig.8

Prediction of water-flowing height based on distributed optical fiber monitoring in fractured zone of overburden strata"

Table 3

Prediction and error of height of collapse zone and fracture zone based on Fv2 fiber"

工作面开挖/ cm实测垮落带高度/mm实测裂隙带高度/mm推测垮落带高度/mm推测裂隙带高度/mm推测导水裂隙带高度/mm绝对误差/mm
156420715410710112015
16846084547083013005
18449010204951000149515
1964951010500100015005
20851010005001000150010
1 张军, 王建鹏. 采动覆岩“三带”高度相似模拟及实证研究[J]. 采矿与安全工程学报, 2014, 31(2): 249-254.
Zhang Jun, Wang Jian-peng. Similar simulation and practical research on the mining overburden roof strata "three-zones" height[J]. Journal of Mining & Safety Engineering, 2014, 31(2): 249-254.
2 李东, 刘生优, 张光德, 等. 鄂尔多斯盆地北部典型顶板水害特征及其防治技术[J]. 煤炭学报, 2017, 42(12): 3249-3254.
Li Dong, Liu Sheng-you, Zhang Guang-de, et al. Typical roof water disasters and its prevention & control technology in the north of Ordos Basin[J]. Journal of China Coal Society, 2017, 42(12): 3249-3254.
3 Palchik V. Influence of physical characteristics of weak rock mass on height of caved zone over abandoned subsurface coalmine[J]. Environmental Geology, 2002, 42(1): 92-101.
4 钱鸣高, 缪协兴, 许家林. 岩层控制中的关键层的理论研究[J]. 煤炭学报, 1996, 21(3): 225-230.
Qian Ming-gao, Miao Xie-xing, Xu Jia-lin. Theoretical study of key stratum in ground control[J]. Journal of China Coal Society, 1996, 21(3): 225-230.
5 钱鸣高, 刘听成. 矿山压力及其控制[M].北京: 煤炭工业出版社, 1991.
6 许家林, 朱卫兵, 王晓振. 基于关键层位置的导水裂隙带高度预计方法[J]. 煤炭学报, 2012, 37(5): 762-769.
Xu Jia-lin, Zhu Wei-bing, Wang Xiao-zhen. New method to predict the height of fractured water-conducting zone by location of key strata[J]. Journal of China Coal Society, 2012, 37(5) : 762-769.
7 张建民, 张凯, 曹志国, 等. 基于采动-爆裂模型的导水裂隙带高度计算方法[J]. 煤炭学报, 2017, 42(6): 1557-1564.
Zhang Jian-min, Zhang Kai, Cao Zhi-guo, et al. Mining-bursting simulation and height calculation method for conducting-water fractured zone[J]. Journal of China Coal Society, 2017, 42(6): 1557-1564.
8 杨达明, 郭文兵, 赵高博, 等. 厚松散层软弱覆岩下综放开采导水裂隙带发育高度[J]. 煤炭学报, 2019, 44(11) : 3308-3316.
Yang Da-ming, Guo Wen-bing, Zhao Gao-bo, et al. Overburden failure height of superhigh seam by fully mechanized caving method[J]. Journal of China Coal Society, 2019, 44(11): 3308-3316.
9 杜文刚, 柴敬, 张丁丁, 等. 采动覆岩导水裂隙发育光纤感测与表征模型试验研究[J]. 煤炭学报, 2021, 46(5): 1565-1575.
Du Wen-gang, Chai Jing, Zhang Ding-ding, et al. Optical fiber sensing and characterization of water flowing fracture development in mining overburden[J]. Journal of China Coal Society, 2021, 46(5): 1565-1575.
10 李振华, 许延春, 李龙飞, 等. 基于BP神经网络的导水裂隙带高度预测[J]. 采矿与安全工程学报, 2015, 32(6): 905-910.
Li Zhen-hua, Xu Yan-chun, Li Long-fei, et al. Forecast of the height of water flowing fractured zone based on BP neural networks[J]. Journal of Mining & Safety Engineering, 2015, 32(6): 905-910.
11 柴华彬, 张俊鹏, 严超. 基于GA-SVR的采动覆岩导水裂隙带高度预测[J]. 采矿与安全工程学报, 2018,35(2): 359-365.
Chai Hua-bin, Zhang Jun-peng, Yan Chao. Prediction of water-flowing height in fractured zone of overburden strata based on GA-SVR[J]. Journal of Mining & Safety Engineering, 2018, 35(2): 359-365.
12 柴敬, 雷武林, 杜文刚, 等. 分布式光纤监测的采场巨厚复合关键层变形试验研究[J]. 煤炭学报, 2020, 45(1): 44-53.
Chai Jing, Lei Wu-lin, Du Wen-gang, et al. Experimental study on the deformation of the thick composite key layer of the stope based on distributed optical fiber monitoring[J]. Journal of China Coal Society, 2020, 45(1): 44-53.
13 侯公羽, 胡涛, 李子祥,等. 基于分布式光纤技术的采动影响下覆岩变形演化规律试验研究[J]. 岩土力学, 2020, 41(3): 970-979.
Hou Gong-yu, Hu Tao, Li Zi-xiang, et al. Experimental study on overburden deformation evolution under mining effect based on distributed fiber optical sensing technology[J]. Rock and Soil Mechanics, 2020, 41(3): 970-979.
14 Sun B Y, Zhang P S, Wu R X, et al. Dynamic detection and analysis of overburden deformation and failure in a mining face using distributed optical fiber sensing[J]. Journal of Geophysics and Engineering, 2018,15(6): 2545-2555.
15 张丹, 张平松, 施斌, 等. 采场覆岩变形与破坏的分布式光纤监测与分析[J].岩土工程学报, 2015, 37(5): 952-957.
Zhang Dan, Zhang Ping-song, Shi Bin, et al. Monitoring and analysis of overburden deformation and failure using distributed fiber optic sensing[J]. Chinese Journal of Geotechnical Engineering, 2015, 37(5): 952-957.
16 董丽丽, 杨丹, 张翔. 基于深度学习的大规模语义文本重叠区域检索[J]. 吉林大学学报: 工学版, 2021, 51(5): 1817-1822.
Dong Li-li, Yang Dan, Zhang Xiang. Large-scale semantic text overlapping region retrieval based on deep learning[J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1817-1822.
17 徐涛, 马克, 刘才华. 基于深度学习的行人多目标跟踪方法[J]. 吉林大学学报: 工学版, 2021, 51(1): 27-38.
Xu Tao, Ma Ke, Liu Cai-hua. Multi object pedestrian tracking based on deep learning[J]. Journal of Jilin University (Engineering and Technology Edition), 2021, 51(1): 27-38.
18 郭立民, 陈鑫, 陈涛. 基于 AlexNet模型的雷达信号调制类型识别[J]. 吉林大学学报: 工学版, 2019, 49(3): 1000-1008.
Guo Li⁃min, Chen Xin, Chen Tao. Radar signal modulation type recognition based on AlexNet model[J]. Journal of Jilin University (Engineering and Technology Edition), 2019, 49(3): 1001-1008.
19 袁强. 采动覆岩变形的分布式光纤检测与表征模拟试验研究[D]. 西安: 西安科技大学能源学院,2017.
Yuan Qiang. Experimental study on detection and representation of mining-induced overburden deformation with distributed optical fiber sensing[D]. Xi'an: School of Energy, Xi'an University of Science and Technology, 2017.
20 毛莺池, 曹海, 何进锋. 面向大坝变形监测的时空一体化预测算法[J]. 计算机科学, 2019, 46(2): 223-239.
Mao Ying-chi, Cao Hai, He Jin-feng. Spatio-temporal integrated forecasting algorithm for dam deformation[J]. Computer Science, 2019, 46(2): 223-239.
21 王俊, 曹俊兴, 尤加春. 基于GRU神经网络的测井曲线重构[J]. 石油地球物理勘探, 2020, 55(3): 510-520, 468.
Wang Jun, Cao Jun-xin, You Jia-chun. Reconstruction of logging traces based on GRU neural network[J]. Oil Geophysical Prospecting, 2020, 55(5): 510-520, 468.
22 谢丽蓉, 王斌, 包洪印, 等. 基于EEMD-WOA-LSSVM的超短期风电功率预测[J]. 太阳能学报, 2021, 42(7): 290-296.
Xie Li-rong, Wang Bin, Bao Hong-yin, et al. Super-short-term wind power forecasting based on EEMD-WOA-LSSVM[J]. Acta Energiae Solaris Sinica, 2021, 42(7): 290-296.
23 郗刘涛. 基于LSTM的采动覆岩变形监测数据预测方法研究[D]. 西安:西安科技大学通信学院,2020.
Xi Liu⁃tao. Research on monitoring data forecasting method of mining-induced overburden deformation based on LSTM[D]. College of Communication and Information Engineering, Xi'an: Xi'an University of Science and Technology, 2020.
24 胡小娟, 李文平, 曹丁涛, 等. 综采导水裂隙带多因素影响指标研究与高度预计[J]. 煤炭学报, 2012, 37(4): 613-620.
Hu Xiao-juan, Li Wen-ping, Cao Ding-tao, et al. Index of multiple factors and expected height of fully mechanized water flowing fractured zone[J]. Journal of China Coal Society, 2012, 37(4): 613-620.
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