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 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.

Determination of Strata Displacement Angles under Subcritical Mining

2012 ◽  
Vol 524-527 ◽  
pp. 274-277
Author(s):  
Hua Bin Chai ◽  
You Feng Zou ◽  
Sheng Yu Li
Keyword(s):  
Integral Method ◽  
Observation Data ◽  
Working Face ◽  
Probability Integral Method ◽  
Theoretical Significance ◽  
Horizontal Strain ◽  
Probability Integral ◽  
Protection Pillar ◽  
Pillar Size

With the surface movement observation data derived from subcritical mining, Matlab curve fitting is applied to calculate predicting parameters of the probability integral method. Other conditions kept unchangeable, the size of the working face is extended to achieve full exploitation degree. The displacement and horizontal strain values are calculated by use of the probability integral method, and the subsidence, slope, curvature, horizontal strain curves are generated, thus, the displacement angles of strata under the condition of critical mining are consequently inversed. It is of theoretical significance and of practical value for delimiting the damaged scope and designing the protection pillar size reasonably.

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.

scholarly journals Higher-Order Iterative Learning Control with Optimal Control Gains Based on Evolutionary Algorithm for Nonlinear System

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yun-Shan Wei ◽  
Xiaofen Yang ◽  
Wenli Shang ◽  
Ying-Yu Chen
Keyword(s):  
Optimal Control ◽  
Evolutionary Algorithm ◽  
Iterative Learning Control ◽  
Fitness Function ◽  
Learning Control ◽  
Absolute Error ◽  
Higher Order ◽  
Iterative Learning ◽  
Discrete Time System ◽  
Maximum Absolute Error

For the nonlinear discrete-time system, higher-order iterative learning control (HOILC) with optimal control gains based on evolutionary algorithm (EA) is developed in this paper. Since the updating actions are constituted by the tracking information from several previous iterations, the suitably designed HOILC schemes with appropriate control gains usually achieve fast convergence speed. To optimize the control gains in HOILC approach, EA is introduced. The encoding strategy, population initialization, and fitness function in EA are designed according to the HOILC characteristics. With the global optimization of EA, the optimal control gains of HOILC are selected adaptively so that the number of convergence iteration is reduced in ILC process. It is shown in simulation that the sum absolute error, total square error, and maximum absolute error of tracking in the proposed HOILC based on EA are convergent faster than those in conventional HOILC.

scholarly journals Three Dimensional Deformation of Mining Area Detection by InSAR and Probability Integral Model

2015 ◽  
Vol XL-7/W4 ◽  
pp. 23-26 ◽  
Author(s):  
H. D. Fan ◽  
X. X. Gao ◽  
D. Cheng ◽  
W. Y. Zhao ◽  
C. L. Zhao
Keyword(s):  
Three Dimensional ◽  
Mining Area ◽  
Integral Model ◽  
Subsidence Basin ◽  
Optimum Parameters ◽  
Probability Integral Method ◽  
Basin Edge ◽  
Probability Integral ◽  
Mining Work ◽  
Deformation Gradients

A new solution algorithm that combined D-InSAR and probability integral method was proposed to generate the three dimensional deformation in mining area. The details are as follows: according to the geological and mining data, the control points set should be established, which contains correct phase unwrapping points in subsidence basin edge generated by D-InSAR and several GPS points; Using the modulus method to calculate the optimum parameters of probability integral prediction; Finally, generate the three dimensional deformation of mining work face by the parameters. Using this method, the land subsidence with big deformation gradients in mining area were correctly generated by example TerraSAR-X images. The results of the example show that this method can generate the correct mining subsidence basin with a few surface observations, and it is much better than the results of D-InSAR.

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