Assessment of Water Sources and Mixing of Groundwater in a Coastal Mine: The Sanshandao Gold Mine, China

Gu, Hengfei; Ma, Feifei; Guo, Jie; Li, Kepeng; Rong, Lu

doi:10.1007/s10230-017-0458-0

Cited by 26 publications

(22 citation statements)

References 31 publications

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“…Seawater and Quaternary water, which have a high hydraulic gradient, infiltrated the −375 m sublevel through bedrock fractures. Simultaneously, the F3 fault and the NW-oriented fractures opened due to mining-induced rock movement and stress redistribution [7,36]. M2 water samples that were affected by the F3 fault mainly received the vertical recharge of Quaternary water and partially received the lateral recharge of seawater.…”

Section: Results Of Multivariate Statistical Analysismentioning

confidence: 99%

“…Physical experiments, numerical simulations, and field measurements have been employed to study mine water inrush [3][4][5]. To precisely predict the probability of water inrush and provide scientific evidence to prevent water inrush in 2 of 17 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].…”

Section: Introductionmentioning

confidence: 99%

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Application of Multivariate Statistical Analysis to Identify Water Sources in A Coastal Gold Mine, Shandong, China

Liu

et al. 2019

Sustainability

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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.

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Section: Results Of Multivariate Statistical Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of Multivariate Statistical Analysis to Identify Water Sources in A Coastal Gold Mine, Shandong, China

Liu

et al. 2019

Sustainability

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“…There are two types of groundwater in the rock fractures of the Xishan gold mine: Ca-rich water and Mg-rich water [37]. The chemical compositions of these two types of water evolved from quaternary water, freshwater, and sea water.…”

Section: Choice Of End-members In the Pca And Hca Modelsmentioning

confidence: 99%

Quantification of Water Sources in a Coastal Gold Mine through an End-Member Mixing Analysis Combining Multivariate Statistical Methods

Liu

et al. 2020

Water

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Mixing calculations have been widely applied to identify sources of groundwater recharge, but these calculations have assumed that the concentrations of end-members are well known. However, the end-members of water remain unclear and are not easily available in practical applications. To better determine end-members and mixing ratios, an end-member mixing analysis combining multivariate statistical methods was used on a large, complex water chemistry dataset collected from the Shashandao gold mine in China. Multivariate statistical methods, including principal component analysis (PCA) and hierarchical cluster analysis (HCA), were applied to determine the specific end-members (these two methods verified each other). On the basis of the identified end-members, a maximum likelihood method was then used to estimate the mixing ratios of the water sources. The combined method proposed in this study can help to identify more accurate end-members and deal with uncertainty in end-member concentrations, and it can also adjust the concentrations until the optimal mixing ratios for the calculation are obtained. This method can be a powerful tool for groundwater management and in predicting water inrush in mining operations.

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“…The isotope ratios are expressed in per mil (% ) relative to VSMOW (Vienna Standard Mean Ocean Water). The analytical precision of δ 18 O and δD were 0.2% and 2% , respectively[15].…”

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confidence: 92%

Source Identification and Quantification of Seepage Water in a Coastal Mine, in China

Duan

Guo

et al. 2019

Water

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The Sanshandao gold mine, which is the largest coastal mine in China, is under threat from seawater intrusion and water inrush. The objective of this study is to determine the water end-members (seawater, freshwater, and brine) of the seepage water in the mine and quantify the proportion of end-members. Non-conservative ions and ion exchange were identified by using hydrogeochemical analysis. Then, the principal component analysis (PCA) was used to identify the end-members of mine water. Three end-members were identified, so a ternary mixture model was applied to compute the mixing ratios. The potential water flow channels and the prevailing supply patterns were inferred by combining the results of mixing ratios with the tectonic and engineering geological conditions. The results indicate that the proportion of seawater in mine water is about 57%, the freshwater is about 16% and the brine is about 27% for the entire mine area, the prevailing supply pattern of seawater was lateral recharge, the water samples which were located in −510 m sublevel or in the northeast of prospecting line 2260 had high proportions of seawater, the freshwater supplied the groundwater mainly through the secondary fractures developed area in a vertical recharge and the influence depth was about −500 m, and F3 was the largest tensile-shear fault in the study area and it was both a watercourse for seawater and fresh water.

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Assessment of Water Sources and Mixing of Groundwater in a Coastal Mine: The Sanshandao Gold Mine, China

Cited by 26 publications

References 31 publications

Application of Multivariate Statistical Analysis to Identify Water Sources in A Coastal Gold Mine, Shandong, China

Application of Multivariate Statistical Analysis to Identify Water Sources in A Coastal Gold Mine, Shandong, China

Quantification of Water Sources in a Coastal Gold Mine through an End-Member Mixing Analysis Combining Multivariate Statistical Methods

Source Identification and Quantification of Seepage Water in a Coastal Mine, in China

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