Mining subsidence prediction for multi-seam and non-rectangular goafs based on probability integral model: a case study from China

2021 ◽  
Vol 14 (13) ◽  
Author(s):  
Shuaiying Peng ◽  
Shengwu Qin ◽  
Ming Wang ◽  
Guangjie Li
Keyword(s):  
Mining Subsidence ◽  
Integral Model ◽  
Subsidence Prediction ◽  
Probability Integral

scholarly journals Field Measurement and Study on Overburden Fracture and Surface Subsidence Law of Solid Filling Mining under Buildings

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zhiyong Fu ◽  
Lujie Zhou ◽  
Kai Yu ◽  
Wanzhen Li ◽  
Hu Chen
Keyword(s):  
Prediction Model ◽  
Surface Subsidence ◽  
Integral Model ◽  
Mine Pressure ◽  
Parameter System ◽  
Pressure Monitoring ◽  
Subsidence Prediction ◽  
Movement Characteristics ◽  
Probability Integral ◽  
Mining Height

In order to predict the surface subsidence scientifically in solid filling mining, it is necessary to establish a complete subsidence prediction model and parameter system according to the evolution law of overburden structure and strata movement characteristics. Mine pressure monitoring and borehole peeping show that the overburden in solid filling mining is mainly a bending zone with relatively complete layered structure, and the overburden only develops a certain height of fault zone near the roof, without collapse. The results show that the surface subsidence pattern of solid filling mining can still be described by probability integral model, and the parameter system of the surface subsidence prediction model based on “equivalent mining height” is further discussed. Finally, the prediction model of surface subsidence established in this paper is applied to an engineering example, and good results are achieved.

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.

scholarly journals A Novel Method of Monitoring Surface Subsidence Law Based on Probability Integral Model Combined with Active and Passive Remote Sensing Data

2022 ◽  
Vol 14 (2) ◽  
pp. 299
Author(s):  
Rui Wang ◽  
Kan Wu ◽  
Qimin He ◽  
Yibo He ◽  
Yuanyuan Gu ◽  
...  
Keyword(s):  
High Precision ◽  
Remote Sensing Data ◽  
Deformation Field ◽  
Integral Model ◽  
Passive Remote Sensing ◽  
Working Face ◽  
Probability Integral ◽  
Complete Deformation ◽  
Sbas Insar ◽  
Made In

For the accurate and high-precision measurement of the deformation field in mining areas using different data sources, the probability integral model was used to process deformation data obtained from an Unmanned Aerial Vehicle (UAV), Differential InSAR (DInSAR), and Small Baseline Subset InSAR (SBAS-InSAR) to obtain the complete deformation field. The SBAS-InSAR, DInSAR, and UAV can be used to obtain small-scale, mesoscale, and large-scale deformations, respectively. The three types of data were all superimposed by the Kriging interpolation, and the deformation field was integrated using the probability integral model to obtain the complete high-precision deformation field with complete time series in the study area. The study area was in the WangJiata mine in Western China, where mining was carried out from 12 July 2018 to 25 October 2018, on the 2S201 working face. The first observation was made in June 2018, and steady-state observations were made in April 2019, totaling four UAV observations. During this period, the Canadian Earth Observation Satellite of Radarsat-2 (R2) was used to take 10 SAR images, the surface subsidence mapping was undertaken using DInSAR and SBAS-InSAR techniques, and the complete deformation field of the working face during the 106-day mining period was obtained by using the UAV technique. The results showed that the subsidence basin gradually expanded along the mining direction as the working face advanced. When the mining advance was greater than 1.2–1.4 times the coal seam burial depth, the supercritical conditions were reached, and the maximum subsidence stabilized at the value of 2.780 m. The subsidence rate was basically maintained at 0.25 m/d. Finally, the accuracy of the method was tested by the Global Navigation Satellite System (GNSS) data, and the medium error of the strike was 0.103 m. A new method is reached by the fusion of active and passive remote sensing data to construct efficient, complete and high precision time-series subsidence basins with high precision.

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