非线性方法在矿井突水水源判别中的应用研究

Water Source Determination of Mine Inflow Based on Non-Linear Method

给出了灰色关联分析及BP神经网络进行判别分析的数学原理和模型.为了有效判别矿井突水水源,基于水化学指标对水源判别的重要性,选择K++Na+,Ca2+,Mg2+,Cl-,SO42+,HCO3-这6项指标作为评价因子.利用峰峰矿业集团梧桐庄煤矿若干水样进行模糊聚类分析,建立含水层评判标准;选取12个典型样本作为训练样本对BP网络进行训练;用2种方法对3个典型水样进行水源判别,并与实际突水水源进行对比.结果表明:BP神经网络与灰色关联分析均能有效判别矿井突水水源,但各有其优缺点,选择何种判别方法可视水化学资料状况而定.

The mathematical principles and models of gray relational analysis and BP neural net- work used to determine the source of water were introduced. In order to determine the water source of mine inflow, the K^＋＋Na^＋ , Ca^2＋ , Mg^2＋ , Cl^- , SO4^2＋ , HCO3^- were selected as evaluation index because of the importance of 6 water hydrochemical element factors. The clustering analysis was performed for water samples from Wutongzhuang mine of Fengfeng mining areas in North China and the determination standards of aquifers was proposed. Based on the training of BP network for 12 selected water samples, two methods mentioned above were used to determine the water sources of 3 typical samples and compared the calculated sources with the actual water source. The results show that both the grey relational analysis（GRA） and BP neural network are appropriate to source determination of mine inflow, but both have the superiority and limitation, which lies on the status of hydrochemical data.