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BI Bo，CHEN Yongchun，XIE Hao，et al.Water inrush warning system of deep limestone in Panxie Mining Area based on multi-source data mining[J].Coal Geology & Exploration，2022，50(2)：81−88. DOI: 10.12363/issn.1001-1986.21.04.0161
Citation: BI Bo，CHEN Yongchun，XIE Hao，et al.Water inrush warning system of deep limestone in Panxie Mining Area based on multi-source data mining[J].Coal Geology & Exploration，2022，50(2)：81−88. DOI: 10.12363/issn.1001-1986.21.04.0161
shu

Water inrush warning system of deep limestone in Panxie Mining Area based on multi-source data mining

  • 1.

    National Engineering Research Center for Coal Mine Gas Control, Huainan 232001, China

  • 2.

    Ping’an Coal Mining Engineering Technology Research Institute Co., Ltd., Huainan 232001, China

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  • Received Date: April 01, 2021
  • Revised Date: September 22, 2021
  • Available Online: January 27, 2022
  • Published Date: January 31, 2022
    Graphical Abstract
    • Abstract
  • Under the long-term water-rock interaction, the hydrochemical composition of deep limestone water in each aquifer is different. Such factors as crustal movement and mining influence have led to the hydraulic connection between aquifers. Major water inrush accidents often occur when the deep high-pressure limestone water bursts into the mine with the shallow limestone water as a channel. On the basis of water level data of 182 surface hydrological observation holes in nine coal mines in Panxie Mining Area of Huainan Coalfield from 2015 to 2018, and the temporal and spatial law of water level change of each hydrological observation hole after the water inrush occurred in Pan’er Mine, we found that the water level change data of hydrological observation holes are more sensitive than the water level elevation data, and the deep limestone water in Panxie Mining Area recharged from bottom to top to the shallow limestone water. The shallow limestone water area with a recharge relationship between aquifer and deep limestone water is identified by clustering analysis algorithm. On the other hand, the water quality test data of more than 7 000 mines are analyzed based on the improved random forest algorithm, and the information on aquifers closely related to the water and hydraulic of deep limestone is identified based on the misclassification data. With the results of clustering analysis of water level change data being analyzed comprehensively, the water inrush risk area of each mine is obtained. Based on the significant classification factors and the hydrochemical characteristics of spatial distribution of each aquifer, we constructed a fast and accurate water inrush warning system by using high precision sensors of temperature, pressure, water level and water quality and other high-precision sensors, as so to monitor the water inrush point of a mine on-line. It provides a fast and reliable basis for water prevention and control measures in construction, and can greatly avoid water inrush accidents in mines, reducing the losses arising from sudden water accidents.
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