Application of Multivariate Statistical Analysis to Identify Water Sources in A Coastal Gold Mine, Shandong, China
Open accessAbstract
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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Chemometrics has been applied to a wide variety of data matrices including water, fungicidal, pharmaceutical, food science, fruits and vegetables processing, Parkinson's disease, plant composition and polymer analysis studies.
“Chemometrics has been applied to a wide variety of data matrices including those collected for water [58], fungicidal [62], pharmaceutical [63], food science [64], fruits and vegetables processing [65], Parkinson’s disease [66], plant composition [67] and polymer analysis [68] studies.”
From Factors Affecting Microalgae Production for Biofuels and the Potentials of Chemometric Methods in Assessing and Optimizing Productivity (2019) · cites this paper as [58]
CA, PCA and discriminant analysis can extract the most relevant information from chemical analysis across a range of selected criteria involved in the production process or assessment of the process.
“CA, PCA and discriminant analysis among other techniques can be applied to extract the most relevant information from chemical analysis of varying nature, across a range of selected criteria involved in the production process or assessment of the process [58].”
From Factors Affecting Microalgae Production for Biofuels and the Potentials of Chemometric Methods in Assessing and Optimizing Productivity (2019) · cites this paper as [58]
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- Authors
- Liu, G., Ma, F., Liu, G., Zhao, H., Guo, J., & Cao, J.
- Affiliations
- Chinese Academy of Sciences (CN)
- Corresponding author
- Ma, Fengshan
- Venue
- Sustainability
- Publisher
- MDPI AG
- Published
- 2019-06-17
- Volume
- 11
- Issue
- 12
- Pages
- 3345
- ISSN
- 2071-1050 (ISSN-L)
- OpenAlex
- W2952289844
- Cited by
- 26
- References
- 33
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 2 · 2023: 3 · 2022: 5 · 2021: 8 · 2020: 3 · 2019: 2
- FWCI
- 4.69
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sustainability (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Fengshan Ma (the National Science Foundation of China (Grant nos. 41772341))
- Topics
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Geoscience and Mining Technology
- Physical Sciences › Environmental Science › Water Science and Technology › Water Quality and Pollution Assessment
- Physical Sciences › Earth and Planetary Sciences › Geochemistry and Petrology › Groundwater and Isotope Geochemistry
- Keywords
- mine water inrush, groundwater classification, Bayes discriminant analysis, clustering
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