基于多变量统计分析的矿井涌水定量判识研究

    Research on quantitative identification of mine water inflow based on multivariate statistical analysis

    • 摘要: 识别矿井涌水来源是煤矿区水害防治的基础和关键,有助于提高涌水防治措施的经济性与有效性。以鄂尔多斯盆地南缘某井田为研究区,在涌水前后采集了11组矿井水样,采用水文地质综合分析方法初步确定涌水来源,并结合地下水化学成分建立涌水数学模型;通过聚类分析、因子分析、多元回归及典型相关分析等数理统计方法,对涌水来源进行定量判识,最终将分析结果与实际勘探结果相互验证。结果表明:研究区涌水的主要来源包括直罗组水、洛河组水、第四系水,其中洛河组水与第四系水主要受地表水补给并沿裂隙进入矿井;第1次涌水样与直罗组水、第四系水及地表水聚为一类,采空区水则独立成类;第2次与第3次涌水样的化学成分与采空区水相似,硫酸根离子(SO42−)和镁离子(Mg2+)质量浓度较高;因子分析得出的直罗组水和采空区水对涌水的贡献度分别为0.989和0.988,多元回归分析得出的地表水和第四系水对涌水影响最为显著,典型相关分析进一步证实回归系数分别为2.764和−3.169。研究区涌水来源主要为地表水、第四系水、直罗组水,研究结果与实际勘探结果高度一致。因此,综合运用多变量统计分析方法,可以实现矿井涌水来源的定量判识。

       

      Abstract: Identifying the sources of mine water gushing is fundamental for preventing water hazards in coal mining areas, enhancing the economic efficiency and effectiveness of prevention measures. Taking a mining area in the southern margin of Ordos Basin as the research area, 11 mine water samples were collected before and after the mine water gushing events. Hydrogeological comprehensive analysis was employed to preliminarily identify the water sources, and a mathematical model was developed using groundwater chemical compositions. The sources of water gushing were quantitatively analyzed using statistical methods such as cluster analysis, factor analysis, multiple regression analysis, and canonical correlation analysis, the analysis results are mutually verified with the actual exploration results. The main sources of water gushing in the study area include Zhiluo Formation water, Luohe Formation water, and Quaternary water. Among these, Luohe Formation water and Quaternary water were primarily recharged by surface water, which infiltrated the mine through fractures. Cluster analysis showed that the first inrush water sample was grouped with Zhiluo Formation water, Quaternary water, and surface water, while goaf water formed a separate cluster. The chemical compositions of the second and third gushing water samples were similar to those of goaf water, the mass concentration of sulfate ions (SO42−) and magnesium ions (Mg2+) is higher. Factor analysis revealed that Zhiluo Formation water and goaf water contributed significantly to the water gushing, with contribution rates of 0.989 and 0.988, respectively. Multiple regression analysis indicated that surface water and Quaternary water had the most significant impacts on water gushing. Canonical correlation analysis further confirmed the strong relationship between gushing water samples and surface water or Quaternary water with regression coefficients of 2.764 and −3.169, respectively. The main sources of water gushing in the study area were identified as surface water, Quaternary water, and Zhiluo Formation water, with the findings highly consistent with actual exploration results. Therefore, integrating multivariate statistical analysis techniques can achieve quantitative identification of water gushing sources.

       

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