矿井突水水源的多元统计分析判别

Multivariate Statistical Analysis and Discrimination of Water Inrush Source in Mine

摘要: 准确地判别突水水源，是做好防治水工作的首要任务。为解决这一问题，收集整理了安徽某矿相关的水文地质资料，以水质分析结果及水文地球化学为基础，选取七个主要离子作为判别指标，基于SPSS软件，将不同含水层的水化学资料中的30个作为训练样本，6个作为预测样本，采用多元统计分析方法中的聚类分析与Bayes逐步判别分析分别建立突水水源判别模型，并对两种方法做出比较。分析结果表明：聚类分析与Bayes逐步判别分析对该矿突水水源都可以做出准确的判别；聚类分析中尤以Ward法准确度最高；Bayes逐步判别剔除了重复信息，判别结果准确率达到100%，是该矿判别突水水源的首选方法。可为矿井突水水源的判别提供一种新的思路。

Abstract: Accurately distinguishing the source of water inrush is the primary task of water prevention and control. In order to solve this problem, the hydrogeological data of a mine in Anhui are collected and sorted out. Based on the results of water quality analysis and hydrogeochemistry, seven main ions are selected as discriminant indexes. Based on SPSS software, 30 hydrochemical data of different aquifers are taken as training samples and 6 as prediction samples. Cluster analysis and Bayesian stepwise discriminant analysis in multivariate statistical analysis method establish discriminant models for water inrush source, and compare the two methods. The results show that both cluster analysis and Bayesian stepwise discriminant analysis can accurately discriminate the source of water inrush in this mine; Ward method is the most accurate method in cluster analysis; Bayesian stepwise discriminant eliminates duplicate information, and the accuracy rate of discriminant results reaches 100%, which is the preferred method to discriminate the source of water inrush in this mine. It can provide a new way to distinguish the source of mine water inrush.