Hu Li, Jie Zheng, Lian Xue, Xue Zhao, Xiuqiang Lei, Xue Gong
{"title":"Inversion of Subsidence Parameters and Prediction of Surface Dynamics under Insufficient Mining","authors":"Hu Li, Jie Zheng, Lian Xue, Xue Zhao, Xiuqiang Lei, Xue Gong","doi":"10.1134/s106273912304021x","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>By combining the advantages of InSAR, Probabilistic Integral Method and Genetic Algorithm, an improved method for dynamic prediction of probability integral parameters is proposed to realize subsidence inversion and prediction in insufficient mining. Firstly, InSAR is used to obtain the time series information of surface deformation in goaf. Then, a genetic algorithm-based parameter inversion model is constructed to invert the subsidence parameters such as subsidence coefficient and influence radius. After that, a dynamic prediction function is established to obtain the complete surface subsidence pattern and dynamic change trend of the mining area. Taking a goaf in Shanxi Province as the experimental object, Sentinel-1A(S-1A) image as the data source, combined with PIM and InSAR data, the parameter inversion model is used to successfully obtain the dynamic change process of mining subsidence parameters. The results show that the dynamic prediction function can achieve a certain effect on surface prediction in insufficient mining, and the parameter inversion model based on genetic algorithm has a high inversion accuracy, which provides a basis for surface prediction in insufficient mining.</p>","PeriodicalId":16358,"journal":{"name":"Journal of Mining Science","volume":"44 7","pages":""},"PeriodicalIF":0.7000,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Mining Science","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1134/s106273912304021x","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MINING & MINERAL PROCESSING","Score":null,"Total":0}
引用次数: 0
Abstract
By combining the advantages of InSAR, Probabilistic Integral Method and Genetic Algorithm, an improved method for dynamic prediction of probability integral parameters is proposed to realize subsidence inversion and prediction in insufficient mining. Firstly, InSAR is used to obtain the time series information of surface deformation in goaf. Then, a genetic algorithm-based parameter inversion model is constructed to invert the subsidence parameters such as subsidence coefficient and influence radius. After that, a dynamic prediction function is established to obtain the complete surface subsidence pattern and dynamic change trend of the mining area. Taking a goaf in Shanxi Province as the experimental object, Sentinel-1A(S-1A) image as the data source, combined with PIM and InSAR data, the parameter inversion model is used to successfully obtain the dynamic change process of mining subsidence parameters. The results show that the dynamic prediction function can achieve a certain effect on surface prediction in insufficient mining, and the parameter inversion model based on genetic algorithm has a high inversion accuracy, which provides a basis for surface prediction in insufficient mining.
期刊介绍：
The Journal reflects the current trends of development in fundamental and applied mining sciences. It publishes original articles on geomechanics and geoinformation science, investigation of relationships between global geodynamic processes and man-induced disasters, physical and mathematical modeling of rheological and wave processes in multiphase structural geological media, rock failure, analysis and synthesis of mechanisms, automatic machines, and robots, science of mining machines, creation of resource-saving and ecologically safe technologies of mineral mining, mine aerology and mine thermal physics, coal seam degassing, mechanisms for origination of spontaneous fires and methods for their extinction, mineral dressing, and bowel exploitation.
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