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Article

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

1
Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
2
Institutions of Earth Science, Chinese Academy of Sciences, Beijing 100029, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(12), 3345; https://doi.org/10.3390/su11123345
Submission received: 22 May 2019 / Revised: 11 June 2019 / Accepted: 11 June 2019 / Published: 17 June 2019
(This article belongs to the Section Sustainable Chemical Engineering and Technology)

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.
Keywords: submarine mine; water inrush; principle component analysis; factor analysis; cluster analysis; discriminant analysis; Bayes model submarine mine; water inrush; principle component analysis; factor analysis; cluster analysis; discriminant analysis; Bayes model

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MDPI and ACS Style

Liu, G.; Ma, F.; Liu, G.; Zhao, H.; Guo, J.; Cao, J. Application of Multivariate Statistical Analysis to Identify Water Sources in A Coastal Gold Mine, Shandong, China. Sustainability 2019, 11, 3345. https://doi.org/10.3390/su11123345

AMA Style

Liu G, Ma F, Liu G, Zhao H, Guo J, Cao J. Application of Multivariate Statistical Analysis to Identify Water Sources in A Coastal Gold Mine, Shandong, China. Sustainability. 2019; 11(12):3345. https://doi.org/10.3390/su11123345

Chicago/Turabian Style

Liu, Guowei, Fengshan Ma, Gang Liu, Haijun Zhao, Jie Guo, and Jiayuan Cao. 2019. "Application of Multivariate Statistical Analysis to Identify Water Sources in A Coastal Gold Mine, Shandong, China" Sustainability 11, no. 12: 3345. https://doi.org/10.3390/su11123345

APA Style

Liu, G., Ma, F., Liu, G., Zhao, H., Guo, J., & Cao, J. (2019). Application of Multivariate Statistical Analysis to Identify Water Sources in A Coastal Gold Mine, Shandong, China. Sustainability, 11(12), 3345. https://doi.org/10.3390/su11123345

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