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
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