Multivariate statistical analysis of lodgepole pine outer bark samples for metallic mineral exploration within the southern Nechako Plateau, British Columbia, Canada

Mining and exploration is an important economic sector within British Columbia and Canada. Biogeochemical mineral exploration is one method that assists in the discovery of economic mineral deposits. The interpretation of biogeochemical data, however, is more complex than conventional exploration samples as it depends on a plant’s interaction with its environment. To reveal patterns related to molybdenum and gold mineralization, multivariate statistical analysis was applied to two suites of biogeochemical samples collected from lodgepole pine outer bark in the southern Nechako Plateau of British Columbia. These samples were collected as part of the Geological Survey of Canada’s Nechako National Mapping Project (from 1996-1998) and a British Columbia Geological Survey’s Interior Plateau Geoscience Project in 1994. One suite of samples contained anomalous molybdenum concentrations and is located proximal to the Endako molybdenum mine. The second suite is anomalous for gold and is within the vicinity of the Blackwater-Davidson gold project. The samples were analysed by instrumental neutron activation analysis for 28 elements, treated as compositions using the log-ratio approach and investigated for their element associations using RQ-mode principal component analysis. Twenty-four elements were chosen in the molybdenum dataset, based on Exploratory Data Analysis, for further statistical procedures. Twenty-five elements were chosen in the gold dataset. Principal Component Analysis identified a number of distinct element associations. The first principal component, from both datasets, shows a complex relationship between plant element uptake and soil composition. Principal component 2 (molybdenum dataset) revealed that molybdenum-cobalt associations in lodgepole pine outer bark may be an indicator of molybdenum mineralization for the southern Nechako Plateau, whereas principal component 2 (gold dataset) revealed the association between gold and arsenic as an indicator of gold mineralization. Further studies are suggested with respect to the nature of molybdenum-cobalt, gold-arsenic, zinc, arsenic-antimony and caesium enrichment within the study area and using principal component analysis on high dimensional, low sample size data.