Volume 50 Issue 3
Mar.  2024
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Article Navigation >  Journal of Mine Automation  > 2024  >  50(3): 71-81, 121
WANG Jiawei, WANG Haijun, WU Hanning, et al. Research on transparency of hidden disaster causing factors in coal mines based on 3D geological modeling technology[J]. Journal of Mine Automation，2024，50（3）：71-81, 121.  doi: 10.13272/j.issn.1671-251x.2023110030
Citation: WANG Jiawei, WANG Haijun, WU Hanning, et al. Research on transparency of hidden disaster causing factors in coal mines based on 3D geological modeling technology[J]. Journal of Mine Automation，2024，50（3）：71-81, 121.  doi: 10.13272/j.issn.1671-251x.2023110030
shu

Research on transparency of hidden disaster causing factors in coal mines based on 3D geological modeling technology

doi: 10.13272/j.issn.1671-251x.2023110030
  • 1.

    Department of Geology, Northwest University, Xi'an 710069, China

  • 2.

    State Key Laboratory of Continental Dynamics, Northwest University, Xi'an 710069, China

  • 3.

    CCTEG Xi'an Research Institute, Xi'an 710077, China

  • Received Date: 2023-11-09
  • Rev Recd Date: 2024-04-07
    • Available Online: 2024-04-11
    Abstract
  • Hidden disaster causing factor is the key issue that restricts the construction of intelligent coal mining. The 3D geological modeling is the main technical means to achieve transparency of hidden disaster causing factors. At present, the 3D geological modeling technology of coal mines mainly relies on geometric modeling and attribute modeling as a supplement, lacking disaster attribute modeling for hidden disaster causing factors. In order to solve the above problems, taking a coal mine in northern Shaanxi as the research object, the 3D geological modeling is conducted on hidden disaster causing factors such as coal seam thickness, roof and floor structural undulations, waterlogged areas, and shallow coal seam topography. Firstly, the digitization of geological data, geophysical exploration, drilling and other achievements are completed. The coal mine geological database is established. Secondly, the DepthInsight modeling software is used to carry out modeling work from two scales: the entire mine and the working face. The drilling layer data is used as the stratigraphic control point, and the stratigraphic sequence is jointly controlled through coal seam and surface contour lines, virtual drilling, and other data. The layer crossing anomalies in the initial layer model is processed. The ground level model and geological body model are constructed. The digital elevation model is used to construct the surface model of the working face. Thirdly, rock mass modeling is used to construct models of goaf and waterlogging areas, and temperature, gas and other information are annotated. The actual mining measurement data of the working face is used to construct a mining measurement model. Finally, the truncated grid model is created. The permeability and water-rich coefficient model of the aquifer is generated through sequential Gaussian simulation to achieve transparent display of hidden hydrological disaster causing factors in the area. Based on a 3D geological model, the distribution and impact of hidden disaster causing factors are analyzed from multiple perspectives such as strata, coal seams and working faces, goaf and its waterlogged areas, and hydrological attributes. The research results can provide a target area for the precise management of hidden disaster causing factors in coal mines, and assist in the construction of intelligent mining in coal mines.

     

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