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Open AccessArticle

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

by Guowei Liu 1,2,3, Fengshan Ma 1,2,*, Gang Liu 4, Jie Guo 1,2, Xueliang Duan 1,2 and Hongyu Gu 5
1
Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
2
Innovation Academy of Earth Science, Chinese Academy of Sciences, Beijing 100029, China
3
College of Earth and Planetary, University of Chinese Academy of Sciences, Beijing 100049, China
4
Xi’an Center of China Geological Survey, Xi’an 710054, Shanxi, China
5
Chengdu Center, China Geological Survey, Chengdu 610081, Sichuan, China
*
Author to whom correspondence should be addressed.
Water 2020, 12(2), 580; https://doi.org/10.3390/w12020580
Received: 14 January 2020 / Revised: 14 February 2020 / Accepted: 18 February 2020 / Published: 20 February 2020
(This article belongs to the Section Wastewater Treatment and Reuse)
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. View Full-Text
Keywords: mixing calculation; ratios; principal component analysis; hierarchical cluster analysis; end-member mixing analysis; maximum likelihood method mixing calculation; ratios; principal component analysis; hierarchical cluster analysis; end-member mixing analysis; maximum likelihood method
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Liu, G.; Ma, F.; Liu, G.; Guo, J.; Duan, X.; Gu, H. Quantification of Water Sources in a Coastal Gold Mine through an End-Member Mixing Analysis Combining Multivariate Statistical Methods. Water 2020, 12, 580.

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