Fusing Minimal Unit Probability Integration Method and Optimized Quantum Annealing for Spatial Location of Coal Goafs

Wei, Tao; Guo, Guangli; Li, Huaizhan; Wang, Lei; Yang, Xiangsheng; Wang, Yuezong

doi:10.1007/s12205-022-1034-0

Cited by 6 publications

(4 citation statements)

References 23 publications

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“…Zha and Li demonstrated these advantages of the GA by comparing with the performance of the least square and pattern search method in parameters inversion in the probability integral method under complex conditions [20,21]. These advantages were also verified with other intelligent methods, including PSO [22], SAA [23], quantum annealing [24][25][26], and the fireworks algorithm [27], The invasive weed optimization [28].…”

Section: Introductionmentioning

confidence: 84%

Mining Subsidence Prediction Model and Parameters Inversion in Mountainous Areas

et al. 2022

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Coal mining in mountainous areas is general in China, especially in Shanxi Province. Under the influence of topography in mountainous areas, surface collapses and landslides caused by underground mining happen at a certain frequency and threaten human lives and assets. Accurate prediction of the movement and deformation of mining subsidence in mountainous areas facilitates the prevention and control of geological disasters. The probability integral method is an official prediction method for mining subsidence prediction in China, while it is lacking in the prediction accuracy in mountainous areas due to the inherent topography. Therefore, a practical prediction model based on slopes slip combined parameters optimization was proposed in this study. The slip subsidence and slip horizontal movement were deduced based on the probability integral method considering the topography (slope angle α < 30°) and geological conditions (loess covered) to build the prediction model. The dynamic step fruit fly optimization algorithm (DSFOA) was applied for parameters inversion about the probability integral method in the proposed prediction model, while the other parameters in the proposed model were determined by mechanics analysis based on the nature of losses. The determination of parameters is more efficient, objective and reasonable, so that the prediction accuracy can be improved. The measured data of the working panel 22,101 located in Taiyuan, Shanxi Province was verified by this practical model, and the result shows that the mean square error of subsidence and the horizontal movement was decreased to 71 mm and 276 mm, respectively, hence, the applicability of the proposed mining subsidence prediction model in mountainous areas is verified. This work will contribute to a comprehensive understanding on the law of surface movement and provide theoretical guidance for surface damage prevention and control in mountainous mining areas.

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Section: Introductionmentioning

confidence: 84%

Mining Subsidence Prediction Model and Parameters Inversion in Mountainous Areas

et al. 2022

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“…Subsequently, based on this working zone, the magnetotelluric sounding method was adopted to verify the delineated goafs by TEM, and the anomalous apparent resistivity surface obtained by these two methods was basically constant. In view of the problems of existing methods for solving the parameters of goaf spatial features, (Wei et al, 2022) proposed a method of identifying the spatial location of underground coal goafs by using the minimum unit probability integration merging method and optimized quantum annealing.…”

Section: Introductionmentioning

confidence: 99%

Early warning technique research of surface subsidence for safe mining in underground goaf in Karst Plateau zone

Zhe,

Hou,

Niu

et al. 2023

Front. Earth Sci.

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Underground mining in Karst Plateau landform area may cause the loss of support for the upper rock stratum, resulting in rock collapse and large-scale subsidence of the ground surface. Also, the formation of a large-scale goafs may further lead to geo-hazards such as collapse, water gushing, slope instability and so on in the area. To reduce the impact of goaf settlement on local strata stability, this paper established a standardized safe mining detection model for goafs based on the geological safety characteristics of mining goafs. With reference to the statistical analysis of the geological conditions in the mining area, a numerical model with 358 goafs and the proposed mining area was established using FLAC3D numerical software. The surface subsidence and variations of plastic zone in the mining area were comprehensively analyzed. The results indicated that there was a correlation between the stability of the mining area and the geological occurrence conditions of the goafs. By quantitatively taking the values from standardized safety mining detection models, the standardized safety mining detection and warning technique was finally established. The findings can provide technical guidance for safety detection and early warning in the whole process of underground goaf mining in Karst Plateau karst development zone.

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“…For instance, Zha et al [12] successfully applied a genetic algorithm to the probability integral method for parameter inversion, demonstrating notable advantages in terms of accuracy and reliability. Subsequent investigations explored the use of the modular vector method [13][14][15], particle swarm algorithms [16][17][18][19], simulated annealing algorithms [20,21], and others [22][23][24][25][26][27] for parameter inversion within the probability integral method framework, all yielding highly satisfactory results. In a comparative analysis of parameter inversion outcomes using various intelligent algorithms, Han Mei et al [28] confirmed that, with an appropriate choice of initial exploration values, the modular vector method excels in accuracy and reliability when contrasted with other algorithms.…”

Section: Introductionmentioning

confidence: 99%

Research on Mining Subsidence Prediction Parameter Inversion Based on Improved Modular Vector Method

Chai,

Xu,

Guan

et al. 2023

Applied Sciences

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In this study conducted in the Shendong mining area, this paper tackles the challenge of estimating mining subsidence parameters in the absence of empirical values. The study employs a tailored pattern recognition method specifically designed for mining subsidence in a specific working face. The goal is to determine a globally approximate optimal solution for these parameters. Subsequently, this study utilizes the approximate optimal solution as an initial exploration value and harnesses the modular vector method to obtain stable, accurate, optimal solutions for the parameters. The results of the study demonstrate the effectiveness of the improved modular vector method. In simulation tests involving the subsidence coefficient, the main influence angle tangent value, the propagation angle of mining influence, and the deviation of the inflection point, the relative errors do not exceed 1.2%, 1.9%, 1.7%, and 7.9%, respectively. Furthermore, when subjected to random errors of less than 20 mm, the relative errors for each parameter remain below 2%. Even in conditions with 200 mm gross error, the relative error for each parameter does not exceed 5.1%, indicating high precision. In an engineering example, the root mean square error of the improved modular vector method’s fitting result is 64.31 mm, constituting a mere 1.79% of the maximum subsidence value. This performance surpasses that of the genetic algorithm (70.47 mm), particle swarm algorithm (72.82 mm), and simulated annealing algorithm (75.45 mm). Notably, the improved modular vector method exhibits superior stability and reduced reliance on the quantity of measured values compared to the three aforementioned algorithms. The inversion analysis of predicted parameters based on the improved modular vector method, coupled with the probability integral method, holds practical significance for enhancing the accuracy of mining subsidence prediction.

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Fusing Minimal Unit Probability Integration Method and Optimized Quantum Annealing for Spatial Location of Coal Goafs

Cited by 6 publications

References 23 publications

Mining Subsidence Prediction Model and Parameters Inversion in Mountainous Areas

Mining Subsidence Prediction Model and Parameters Inversion in Mountainous Areas

Early warning technique research of surface subsidence for safe mining in underground goaf in Karst Plateau zone

Research on Mining Subsidence Prediction Parameter Inversion Based on Improved Modular Vector Method

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