Research on probability integration parameter inversion of mining-induced surface subsidence based on quantum annealing

Wang, Lei; Wei, Tao; Li, Nan; Chi, Shenshen; Jiang, Chuang; Fang, Suyang

doi:10.1007/s12665-018-7927-z

Cited by 19 publications

(13 citation statements)

References 9 publications

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“…Therefore, two groups of experiments were designed. (1) The cyan area was used to obtain accurate PIM parameters [8]; the aim of this group of experiments was to provide the PIM parameters of this area for the second experiment to verify the experimental results. (2) The red was used to obtain the positioning parameters and PIM parameters of the underground goaf to verify the feasibility of the proposed method.…”

Section: Sar Data Processingmentioning

confidence: 99%

“…Therefore, in this work, we used the area indicated by the cyan frame in Figure 6 to obtain the PIM parameters of the area using accurate goaf parameters and surface observations of deformation obtained by InSAR. The inversion method is shown in [8], and the inversion result was q = 0.83, b = 0.3, tanβ = 1.8.…”

Section: Inversion Of Goaf Parametersmentioning

confidence: 99%

“…Inversion research on mining subsidence has two main aspects. Firstly, the PIM parameters are obtained by inversion from the underground mining information and surface subsidence observation data, and the mining subsidence caused by the other working faces can be predicted by the PIM [8]. Secondly, using the known PIM model parameters and surface subsidence observation values, the goaf positions in the mining area can be obtained by inversion [9].…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Fan

et al. 2021

Remote Sensing

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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%.

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Section: Sar Data Processingmentioning

confidence: 99%

Section: Inversion Of Goaf Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zhang

Fan

et al. 2021

Remote Sensing

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“…Although this method is simple to operate and has high prediction accuracy, it requires a lot of measured data to build a model, and the model is built for a specific mining area and cannot be used in other mining areas [13]- [14].The second type are the influence function method. In mining subsidence, scholars have proposed a variety of influence functions, such as generalized influence function, Probability integral method (PIM) and its improved model, and n-k-g based influence function, etc [15]. Among them, the most commonly used is the PIM model,The PIM model has the advantages of high prediction accuracy, few parameters, and practical significance of the parameters, but there will be a phenomenon of poor prediction at the boundary shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Research on Prediction Model of Mining Subsidence in Thick Unconsolidated Layer Mining Area

Chi

Wang

et al. 2021

IEEE Access

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Accurately forecasting the scope of coal mining subsidence area is of great significance to the protection of surface structures. Due to the peculiarities of the unconsolidated layers rock formation, the surface movement of the thick unconsolidated layers mining area converges slowly at the boundary of the basin, and the boundary of the subsidence basin is larger than under conventional conditions. It is found that the existing models have a poor prediction effect at the boundary, and the predicted subsidence basin range is smaller than the actual basin range. To solve this problem, a new surface deformation prediction model based on Boltzmann function (IB) is proposed in this paper. Aiming at the problem that the model function is highly nonlinear and difficult to obtain parameters, the multi-population genetic algorithm (MPGA) is introduced into the parameter solution of the prediction model, and the parameter calculation model based on multi-population genetic algorithm (MPGAIB) is constructed. The simulation experiment and engineering example analysis show that both the overall fitting effect and the fitting effect at the boundary of IB model are closer to the actual situation, The MPGAIB model has good ability to anti-random error and gross error, and the result is stable. INDEX TERMSmining subsidence, parameter calculation, Probability integral method, prediction model, thick unconsolidated layers List of abbreviations IB-Improved Boltzmann function MPGA-Multi-population genetic algorithm MPGAIB -Multi-population genetic algorithm parameter model PIM-Probability integral method SIE-Semi-infinitive Extraction FE-Finite Extraction FEAS-Finite extraction along strike FEAD-Finite extraction along dip NUM-The number of populations LNUM-The number of individuals in each population GGAP-The generation gap NVAR-The dimension of the variable Pc-The crossover probability Pm-The mutation probability MAXGEN -The optimal individual maintains the least algebra RMSE-Root Mean

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“…Intelligent optimization algorithms are a class of algorithms that have emerged in recent years. These algorithms are based on the evolutionary rules or laws in nature, such as GA, particle swarm optimization, simulated annealing algorithms and so on [13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Parameter Solving of Probability Integral Method Based on Improved Genetic Algorithm

Liang

et al. 2021

Teh. vjesn.

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The probability integral method (PIM) is the main method for mining subsidence prediction in China. Parameter errors and model errors are the main sources of error in the application of the probability integral method. There are many surface subsidence problems caused by coal mining. In order to improve the accuracy and operating efficiency of the genetic algorithm (GA) in calculating the parameters of the PIM, this paper proposes an improved genetic algorithm (IGA) by adding the dynamic crossover and mutation rate to the traditional GA. Made improvements to the shortcomings of random crossover and mutation rate of all individuals in the population in the original algorithm.Through simulation experiments, it is confirmed that the IGA improves the calculation efficiency and accuracy of the traditional GA under the same conditions.The IGA has higher accuracy, reliability, resistance to gross interference and resistance to missing observation points. This method is obviously superior to direct inversion and conventional optimization inversion algorithms, and effectively avoids the dependence on the initial value of the probabilistic integral method parameter.

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Research on probability integration parameter inversion of mining-induced surface subsidence based on quantum annealing

Cited by 19 publications

References 9 publications

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

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

Research on Prediction Model of Mining Subsidence in Thick Unconsolidated Layer Mining Area

Parameter Solving of Probability Integral Method Based on Improved Genetic Algorithm

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