ZENG Yifan, MEI Aoshuang, WU Qiang, HUA Zhaolai, ZHAO Di, DU Xin, WANG Lu, LÜ Yang, PAN Xu. Source discrimination of mine water inflow or inrush using hydrochemical field and hydrodynamic field tracer simulation coupling[J]. Journal of China Coal Society, 2022, 47(12): 4482-4494.
Citation: ZENG Yifan, MEI Aoshuang, WU Qiang, HUA Zhaolai, ZHAO Di, DU Xin, WANG Lu, LÜ Yang, PAN Xu. Source discrimination of mine water inflow or inrush using hydrochemical field and hydrodynamic field tracer simulation coupling[J]. Journal of China Coal Society, 2022, 47(12): 4482-4494.

Source discrimination of mine water inflow or inrush using hydrochemical field and hydrodynamic field tracer simulation coupling

  • Accurate source discrimination of mine water inflow or inrush is of great significance to ensure the sustain⁃ able and safe production of coal mines. A comprehensive source identification technique of mine water inflow or inrush based on the hydrochemical field machine learning analysis and hydrodynamic field reverse tracer simulation is proposed,in order to make up for the lack of support and verification of the actual water cycle in the identification results of the present methods, as well as the insufficient combination of mine water inflow or inrush phenomenon and mine three⁃dimensional hydrogeological model in the actual mining process. Firstly,the principle of hydrogeochemistry is used to reveal the hydrochemical characteristics of mine water inflow or inrush and its possible source aquifer(water body),and the similarity of characteristics is used to qualitatively analyze the source of water inrush.Then,the machine learning algorithm is used to quantitatively identify the source of water inflow or inrush. Finally,the numerical model of the seepage field is established to realize the re⁃verification of water source and the visual output of water path. Tak⁃ ing the Caojiatan Coal Mine as an engineering example,this method is used to identify the water inflow sources of No.122108 and No.122109 working faces. Research results show that the anions in groundwater in the study area are always dominated by HCO-3 ,while the cations show a trend of transitioning from the dominance of Ca2+ to the domi⁃ nance of Na+ + K+ with the increase of depth. Support Vector Machine ( SVM ) requires an extra Genetic Algorithm(GA)to optimize penalty coefficient c and kernel function parameter g. Random Forest(RF)can obtain satis⁃ factory performance without complicated parameter setting and optimization, and has higher accuracy. Visualiza⁃ tion model of mine water inflow or inrush seepage field reverse tracing shows that the NO.122109 working face is loca⁃ ted nearby in the skylight of laterite aquifuge,and there is a situation that groundwater in the Quaternary aquifer flows into working face through water⁃conducting fractured zone. The result of the NO.122108 working face water inflow i⁃ dentified by the method is the groundwater of the Zhiluo Formation and the Yan’an Formation aquifers,and the NO. 122109 working face is the groundwater of the Quaternary aquifer. The identification results are consistent with the ac⁃ tual situation of the coal mine.
  • loading

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return