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

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.

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

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.

Method to Calculate Mining-Induced Fracture Based on the Movement and Deformation of Overburden Strata

Mining-induced fracture of overburden strata is intimately related to underground water disasters in coal mining. In this work, we develop an analytical model that uses the probability integral method to calculate the subsidence of the subsurface and the overburden strata. In the developed model, according to the failure characteristics of the mining-induced strata, the horizontal deformation of the strata is expressed by the tensile rate of the elastic plate’s neutral plane to reflect the degree of fracture initiation and expansion. The distribution of the water-flowing fractured zone (WFZ) in the overburden strata is calculated by substituting the probability integral function of overburden strata movement into the equation of layer tensile rate. The panel 31071 in Peigou coal mine is taken as a case study, and the height of the water-flowing fractured zone (HWFZ) is determined by the proposed method. Conventional empirical methods and the proposed method are used to predict HWFZ in panels with mining schemes, and the results show that the model is particularly advantageous for inclined coal seam mining where the inclined mining size gradually increases and the coal seam burial depth gradually decreases. In such kind of situations, the overburden strata movement and deformation intensify and the mining fracture develops further with the progress of mining, a feature considered poorly by conventional empirical methods but well represented in the proposed method.

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

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.

Position Inversion of Goafs in Deep Coal Seams Based on DS-InSAR Data and the Probability Integral Methods

The goafs caused by coal mining cause great harm to the surface farmland, buildings, and personal safety. The existing monitoring methods cost a lot of workforce and material resources. Therefore, this paper proposes an inversion approach for establishing the locations of underground goafs and the parameters of the probability integral method (PIM), thus integrating distributed scatter interferometric synthetic aperture radar (DS-InSAR) data and the PIM. Firstly, a large amount of surface deformation observation data above the goaf are obtained by DS-InSAR, and the line-of-sight deformation is regarded as the true value. Secondly, according to the obtained surface deformations, the ranges of eight goaf location parameters and three PIM parameters are set. Thirdly, a correlation function between the surface deformation and the underground goaf location is constructed. Finally, a particle swarm optimization algorithm is used to search for the optimal parameters in the range of the set parameters to meet the requirement for minimum error between the surface deformation calculated by PIM and the line-of-sight deformation obtained by DS-InSAR. These optimal parameters are thus regarded as the real values of the position of the underground goaf and the PIM parameters. The simulation results show that the maximum relative error between the position of the goaf and the PIM parameters is 2.11%. Taking the 93,604 working face of the Zhangshuanglou coal mine in the Peibei mining area as the research object and 12 Sentinel-1A images as the data source, the goaf location and PIM parameters of the working face were successfully inverted. The inversion results show that the maximum relative error in the goaf location parameters was 16.61%, and the maximum relative error in the PIM parameters was 26.67%.