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

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.

Water quality assessment and source identification of the Shuangji River (China) using multivariate statistical methods

“…The link distance is reported as Dlink/Dmax, which represents the quotient of the link distance divided by the maximum distance and multiplied by 100 in a specific case to standardize the link distance on the y-axis. The standardized data were clustered by the Ward method and square Euclidean distance [ 41 ].…”

Factors Affecting Microalgae Production for Biofuels and the Potentials of Chemometric Methods in Assessing and Optimizing Productivity

“…Common pattern recognition methods include: Exploratory data analysis (e.g., principal component analysis: PCA), unsupervised techniques (e.g., cluster analysis: CA) and supervised techniques (e.g., soft independent modeling of class analogy: SIMCA) [56]. 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].…”

“…The ability of instrumental analysis techniques such as chromatography, spectroscopy, spectrophotometry and microscopy to generate large volumes of information, even from a single chemical or environmental sample creates a need to harmonize such information into a meaningful explanation [59,60,61]. 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.…”

A Normal Cloud Model-Based Method for Risk Assessment of Water Inrush and Its Application in a Super-Long Tunnel Constructed by a Tunnel Boring Machine in the Arid Area of Northwest China

“…Water inrush has great perniciousness in tunnel excavation [1,2]; particularly for a tunnel constructed by a tunnel boring machine (TBM), it may not only submerge the mechanical equipment of the TBM, which is worth tens of millions of dollars, but also seriously endanger the safety and life of the builders in the tunnel [3,4]. For the construction of tunnels in arid areas, water inrush also leads to a decline in groundwater level, surface vegetation degradation, and land desertification [5].…”