Principal components analysis and K-means clustering of till geochemical data: Mapping and targeting of prospective areas for lithium exploration in Västernorrland Region, Sweden.

[Display omitted] • PCA and K-means clustering are separately useful for the interpretation and classification of multivariate geochemical data. • It has significant potential for undiscovered Li mineralization in Västernorrland based on the results of PCA and K-means clustering. • Expert knowledge must be involved in the selection of variables and estimation of the optimal numbers of clusters. • Till geochemical data were useful for identification of potential area in Västernorrland, Sweden. To achieve the demand for elements used for the green transition energy, such as lithium, it is necessary to recognize the spatial distribution of the concentrations of these elements in different earth materials such as bedrock and soil and to identify areas with anomalous concentrations of such elements (i.e., mineralization) for further exploration and hopefully exploitation. This study carried out multivariate statistical analyses on compositional (i.e., element concentration) data from till samples to recognize areas that likely contain lithium pegmatite mineralization in the Västernorrland region, central Sweden. We applied principal components analysis (PCA) and K-means clustering techniques to reveal regional-scale patterns in the till geochemical data. We demonstrate that these two methods have potential for recognition of geochemical anomalies related to the underlying bedrock geology as well as to mineralization. The results of PCA- and K-means clustering were validated using known occurrences of lithium mineralization. Two different datasets were compared; one containing all available geochemical data and the second containing only available trace elements in the dataset and it was found that anomalous clusters of samples defined by K-means clustering have anomalous multi-element signatures defined by robust PCA. This demonstrated that principal components are the continuous solutions to the discrete cluster members for the K-means clustering. The results show that both PCA and K-means clustering of till geochemical datasets at the early stages of exploration and target generation may reveal useful information that can be used to identify potential areas for more detailed mapping or exploration activities. [ABSTRACT FROM AUTHOR]