Research on 3D Laser Scanning Monitoring Method for Mining Subsidence Based on the Auxiliary for Probability Integral Method

2021 ◽  
Author(s):  
Lei Wang ◽  
Zhu Shangjun ◽  
Jiang Chuang ◽  
Li Jingyu ◽  
Jiang Kegui ◽  
...  
Keyword(s):  
Integral Method ◽  
Laser Scanning ◽  
Mining Subsidence ◽  
3D Laser Scanning ◽  
Monitoring Method ◽  
Probability Integral Method ◽  
Probability Integral

scholarly journals A prediction model of mining subsidence in thick loose layer based on probability integral model

2020 ◽  
Vol 24 (3) ◽  
pp. 367-372
Author(s):  
Jingxian Li ◽  
Xuexiang Yu ◽  
Ya Liang
Keyword(s):  
Prediction Model ◽  
Integral Method ◽  
Mining Subsidence ◽  
Mining Depth ◽  
Probability Integral Method ◽  
Mining Conditions ◽  
Probability Integral ◽  
Influence Radius ◽  
Loose Layer ◽  
Main Influence

The probability integral method is the most commonly used mining subsidence prediction model, but it is only applicable to ordinary geological mining conditions. When the loose layer in the geological mining conditions where the mining face is located is too thick, many inaccurate phenomena will occur when the movement deformation value is predicted by the probability integral method. The most obvious one is the problem that the predicted value converges too fast compared with the measured value in the edge of the sinking basin. In 2012, Wang and Deng proposed a modified model of probability integral method for the marginal errors in the model of probability integral method and verified the feasibility of the method through examples. In this paper, the method is applied to the prediction of surface movement under thick and loose layers after modified. Through practical application, it is found that due to the angle between the working face and the horizontal direction, the average mining depth in the strike direction is different from the average mining depth in the inclined direction, and the main influence radius of the two main sections are often. Therefore, based on this problem, this paper divides the main influence radius into trend and tendency and adjusts the parameters in the model to find the rules of the parameters. The original method uses a dynamic scale factor to adjust the predicted shape of the graph by adjusting the sinking coefficient. This study is aimed to set the scale factor to 0.5 and fix the value of the sinking factor, and propose to adjust the integral range and then adjust the shape of the graph to make it more in line with the actual measurement situation.

scholarly journals Improving Boundary Constraint of Probability Integral Method in SBAS-InSAR for Deformation Monitoring in Mining Areas

2021 ◽  
Vol 13 (8) ◽  
pp. 1497
Author(s):  
Mengyao Shi ◽  
Honglei Yang ◽  
Baocun Wang ◽  
Junhuan Peng ◽  
Zhouzheng Gao ◽  
...  
Keyword(s):  
Time Series ◽  
Integral Method ◽  
Surface Deformation ◽  
Deformation Monitoring ◽  
Mining Subsidence ◽  
Economic Losses ◽  
Mining Areas ◽  
Boundary Constraint ◽  
Probability Integral Method ◽  
Probability Integral

Coal-mining subsidence causes ground fissures and destroys surface structures, which may lead to severe casualties and economic losses. Time series interferometric synthetic aperture radar (TS-InSAR) plays an important role in surface deformation detection and monitoring without the restriction of weather and sunlight conditions. In addition, the probability integral method (PIM) is a surface movement model that is widely used in the field of mining subsidence. In recent years, the integration of TS-InSAR and the PIM has been extensively studied. In this paper, we propose a new method to estimate mining subsidence with the PIM based on TS-InSAR results. This study focuses on the improvement of a boundary constraint and dynamic parameter estimation in the PIM through the inversion of the line-of-sight (LOS) time series deformation derived by TS-InSAR. In addition, 45 Sentinel-1A images from 17 June 2015 to 27 December 2017 of a coal mine in Jiaozuo are utilized to acquire the surface displacement. We apply a time series deformation analysis using small baseline subsets (SBAS) and place the results into an improved PIM to estimate the mining parameters. The simulated mining subsidence is highly consistent with the leveling data, exhibiting an RMSE of 0.0025 m. Compared with the conventional method, the proposed method is more accurate in discovering displacement in mining areas. In the final section of this paper, some sources of error that affect the experiment are discussed.

Robust estimation of model parameters of the probability integral method based on CA-rPSO

Survey Review ◽  
2021 ◽  
pp. 1-11
Author(s):  
Zhengshuai Wang ◽  
Chuanguang Zhu ◽  
Hongzhen Zhang ◽  
Jianrong Kang ◽  
Jinshan Hu
Keyword(s):  
Robust Estimation ◽  
Integral Method ◽  
Model Parameters ◽  
Probability Integral Method ◽  
Probability Integral ◽  
Estimation Of Model Parameters

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.

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