SOM modelling and prospectivity mapping of surface geochemical data in Au and multi-metal mineral exploration: example from northern Finland

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Regional till and weathered bedrock geochemical data sets, provide a basis for data analysis and modelling in the glaciated terrains. These large-scale surface geochemical data sets have great potential in mineral exploration, especially when machine learning and clustering methods are used to reduce the dimensionality of multivariate data sets. Here, self-organising maps (SOM) followed by the k-means clustering were used to create SOM of the target areas for initial modelling and prospectivity mapping of Au prospecting in central Lapland, northern Finland. Due to the nature of the till and weathered bedrock data being legacy data, levelling effort was made between the map sheets. The targeting till geochemical data set did not contain the typical indicator elements for gold. Instead, elements associated with the gold deposits, within the study area, were used. Indicator associations in this study were Ni-Co with possible Cu. Resulting elemental clusters from SOM were assigned as interesting clusters according to their distribution of elements. For the till, two potential clusters were deemed, Ni-Co-Cr and Cu-V-Co. For the weathered bedrock, three clusters were specified, Ni-Co-Cr, V-Cu, and Cu-Co. This study shows the potential of using legacy data sets for early targeting stages of mineral exploration, potentially reducing the footprint of mineral exploration.