scholarly journals Extraction of Irregularly Shaped Coal Mining Area Induced Ground Subsidence Prediction Based on Probability Integral Method

2020 ◽  
Vol 10 (18) ◽  
pp. 6623
Author(s):  
Xianfeng Tan ◽  
Bingzhong Song ◽  
Huaizhi Bo ◽  
Yunwei Li ◽  
Meng Wang ◽  
...  
Keyword(s):  
Coal Mining ◽  
Integral Method ◽  
Mesh Size ◽  
Mining Area ◽  
Ground Subsidence ◽  
Data Set ◽  
Coal Mining Area ◽  
Subsidence Prediction ◽  
Probability Integral Method ◽  
Probability Integral

Underground coal mining-induced ground subsidence (or major ground vertical settlement) is a major concern to the mining industry, government and people affected. Based on the probability integral method, this paper presents a new ground subsidence prediction method for predicting irregularly shaped coal mining area extraction-induced ground subsidence. Firstly, the Delaunay triangulation method is used to divide the irregularly shaped mining area into a series of triangular extraction elements. Then, the extraction elements within the calculation area are selected. Finally, the Monte Carlo method is used to calculate extraction element-induced ground subsidence. The proposed method was tested by two experimental data sets: the simulation data set and direct leveling-based subsidence observations. The simulation results show that the prediction error of the proposed method is proportional to mesh size and inversely proportional to the amount of generated random points within the auxiliary domain. In addition, when the mesh size is smaller than 0.5 times the minimum deviation of the inflection point of the mining area, and the amount of random points within an auxiliary domain is greater than 800 times the area of the extraction element, the difference between the proposed method-based subsidence predictions and the traditional probability integral method-based subsidence predictions is marginal. The measurement results show that the root-mean-square error of the proposed method-based subsidence predictions is smaller than 3 cm, the average of absolute deviations of the proposed method-based subsidence predictions is 2.49 cm, and the maximum absolute deviation is 4.05 cm, which is equal to 0.75% of the maximum direct leveling-based subsidence observation.

Analysis of ground subsidence at a coal-mining area in Huainan using time-series InSAR

2015 ◽  
Vol 36 (23) ◽  
pp. 5790-5810 ◽  
Author(s):  
Zhengjia Zhang ◽  
Chao Wang ◽  
Yixian Tang ◽  
Hong Zhang ◽  
Qiaoyan Fu
Keyword(s):  
Time Series ◽  
Coal Mining ◽  
Mining Area ◽  
Ground Subsidence ◽  
Coal Mining Area

Use of D-InSAR Technique for Monitoring Ground Subsidence in the Yanzhou Coal Mining Area (China)

2010 ◽  
Vol 34-35 ◽  
pp. 756-760 ◽  
Author(s):  
Zhi Yong Wang ◽  
Jin Zhi Zhang
Keyword(s):  
Coal Mining ◽  
Mining Area ◽  
Mining Subsidence ◽  
Ground Subsidence ◽  
Srtm Dem ◽  
Alos Palsar ◽  
Coal Mining Area ◽  
L Band

In this paper, it monitored ground subsidence in the coal mining area using two-pass D-InSAR technique. We obtained 9 ALOS PALSAR single-look complex (SLC) images in Yanzhou coal mining area from December 2007 to Februay 2009. Based on SAR interferometric pairs and SRTM DEM, we detected the subsided areas and got the vertical subsided quantity. We got the ground subsidence maps in different stages from 2007 to 2009. Several important subsided areas were selected and then analyzed in detail. It analyzed the general laws of mining subsidence. The results indicated that two-pass D-InSAR technique based on L-band PALSAR data and SRTM DEM is a very simple, rapid and efficient way to detect and to monitor ground subsidence in the coal mining area, even in the areas with vegetation covered.

scholarly journals Probability Integral Method Parameter Determination by SBAS-InSAR Technology and GWO Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Tieming Liu ◽  
Tongkang Zhang ◽  
Lichuan Chen ◽  
Weiming Liao ◽  
Yun Shi ◽  
...  
Keyword(s):  
Integral Method ◽  
Fitness Function ◽  
Absolute Error ◽  
Mining Area ◽  
Maximum Absolute Error ◽  
Working Face ◽  
Probability Integral Method ◽  
Median Error ◽  
Probability Integral ◽  
Sbas Insar

This paper proposed a method based on the SBAS-InSAR and gray wolf optimization algorithm aiming at the time-consuming and laborious defects of the traditional method used to obtain the expected parameters of the probability integral method and the shortcomings of the InSAR technology in the field of large gradient deformation detection in the mining area. The fitness function of the algorithm was established based on the geometric relationship between the radar side imaging and the three-dimensional model of the probability integral method. The stable sinking point of the settlement boundary obtained by SBAS-InSAR was used as the input value for the calculation of the predicted parameters of the probability integral method. Firstly, the simulation experiment was employed for the simulation of the direction of the InSAR line of sight combined with the geological mining conditions of the assumed working face, thereby obtaining the probability integral prediction parameters of the working face. Consequently, the maximum relative error of q , b , tanβ, and θ 0 does not exceed 8%, and that of S 1 , S 2 , S 3 , and S 4 does not exceed 35.5% (low parameter sensitivity). The error of the LOS-direction deformation fitting is 0.076 m, which meets the tolerance requirements, and the result is trustworthy. At last, the parameter finding method is applied to the engineering example, that is, the 112201 working face of Xiaobaodang Coal Mine in the northern Shaanxi mining area. The settlement value of the stable boundary point is obtained based on the SBAS-InSAR results, which is substituted into the fitness function. And the GWO optimization algorithm is used for optimization and parameter finding; the probability integral expected parameters of the working face are calculated as q = 0.63 , b = 0.37 , tan β = 2.76 , θ 0 = 83.94 , S 1 = − 36.34   m , S 2 = 26.69   m , S 3 = − 45.64   m , and S 4 = 39.62   m . Substitute the obtained parameters into the probability integral model for the prediction of the vertical and horizontal displacements of the working face, and verify its accuracy with the GPS measured data. The results showed that the maximum absolute error of vertical displacement reached 116 mm, the median error was 63 mm, and the maximum absolute error of north-south horizontal movement reached 56 mm; meanwhile, the median error was 23 mm, the maximum absolute error of east-west horizontal movement reached 61 mm, and the median error was 29 mm; all the above parameters are within the tolerance range, indicating that the method for the calculation of probability integral parameters proposed in this paper is applicable in actual engineering.

scholarly journals Improving Reliability of Prediction Results of Mine Surface Subsidence of Northern Pei County for Reusing Land Resources

2020 ◽  
Vol 10 (23) ◽  
pp. 8385
Author(s):  
Yafei Yuan ◽  
Huaizhan Li ◽  
Haojie Zhang ◽  
Yiwei Zhang ◽  
Xuewei Zhang
Keyword(s):  
Integral Method ◽  
Large Scale ◽  
Surface Subsidence ◽  
Correct Selection ◽  
Land Resources ◽  
Subsidence Prediction ◽  
Probability Integral Method ◽  
Mining Conditions ◽  
Probability Integral ◽  
Prediction Parameters

The accurate prediction of mine surface subsidence is directly related to the reuse area of land resources. Currently, the probability integral method is the most extensive method of surface subsidence prediction in China. However, its prediction precision largely depends on the accuracy of the selected parameters. When the mining area lacks measured data, or the geological and mining conditions change, particularly for large-scale surface subsidence prediction, the reliability of the prediction of surface subsidence is poor. Moreover, there is a lack of a systematic summary of the correct selection of prediction parameters. Based on this, the paper systematically investigated the influence of geological and mining conditions on the prediction parameters of the probability integral method. The research findings were obtained via theoretical analysis. The research outcomes can provide a scientific basis for properly selecting the prediction parameters of the probability integral method. Last, the results of this paper can be applied to predict the surface subsidence of Pei County in the north, laying the foundation for the integration of Pei County.

Analysis of ground subsidence in coal mining area using SAR interferometry

2008 ◽  
Vol 12 (3) ◽  
pp. 277-284 ◽  
Author(s):  
Jin Baek ◽  
Sang-Wan Kim ◽  
Hyuck-Jin Park ◽  
Hyung-Sup Jung ◽  
Ki-Dong Kim ◽  
...  
Keyword(s):  
Coal Mining ◽  
Mining Area ◽  
Sar Interferometry ◽  
Ground Subsidence ◽  
Coal Mining Area
Sign in / Sign up

Export Citation Format

Share Document