Abstract
Submarine mine water inrush has become a problem that must be urgently solved in coastal gold mining operations in Shandong, China. Research on water in subway systems introduced classifications for the types of mine groundwater and then established the functions used to identify each type of water sample. We analyzed 31 water samples from −375 m underground using multivariate statistical analysis methods. Cluster analysis combined with principle component analysis and factor analysis divided water samples into two types, with one type being near the F3 fault. Principal component analysis identified four principle components accounting for 91.79% of the total variation. These four principle components represented almost all the information about the water samples, which were then used as clustering variables. A Bayes model created by discriminant analysis demonstrated that water samples could also be divided into two types, which was consistent with the cluster analysis result. The type of water samples could be determined by placing Na+ and CHO3− concentrations of water samples into Bayes functions. The results demonstrated that F3, which is a regional fault and runs across the whole Xishan gold mine, may be the potential channel for water inrush, providing valuable information for predicting the possibility of water inrush and thus reducing the costs of the mining operation.
Highlights
With continuous technological development and progress, the mineral resources of the Earth— especially those exploited with good geological conditions and shallow burial depths—have been mined and used in large quantities
On the basis of the hydrochemical data set, principle component analysis (PCA), cluster analysis, factor analysis, and discriminant analysis are multivariate methods that are extremely helpful for characterizing groundwater
M1 water samples were recharged by Quaternary water through the F3 fault in the vertical direction, and M2 water samples weFriegFuipgrauerr5tel.y5F.raFecactchotraorsgcsoecrodersebsoyobQbtatuainaineteddrbnbyayrcycluluwsstateetrrearannianallythsies:v((eaar))tFFicaaa11lvvdss.i.rFeFaca2t2iaoannnddt(hb(rb)o)FuaFg1ah1vsvt.hsFe. aFF3a.33Ff.aaF1u,alF1ta,a2Fn,ada2n,mdanodstly byFsaeF3aaaw3cacaoctceuornuitnnftotfrho8er38l.a35.t%5e%roafol ftdhtihereetcotottiatoalnlvvatahrririaaonnuccgee.h
Summary
With continuous technological development and progress, the mineral resources of the Earth— especially those exploited with good geological conditions and shallow burial depths—have been mined and used in large quantities. Scholars have conducted extensive studies on identifying mine water resources. A conventional coefficients and scenario analysis methodology combined with the Bayesian network has been used to discriminate water sources and conduct probability estimation [1,2]. To precisely predict the probability of water inrush and provide scientific evidence to prevent water inrush in mine districts, correct and effective methods to identify water sources are urgently required [6,7,8,9]. Hydrochemical analysis, which is simple, efficient, and inexpensive, is usually used to identify water sources in rivers and mine operations [10,11,12]. Water resources have been identified using a variety of methods. Few scholars have used combined multivariate statistical methods to estimate mine water inrush sources
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