Data‐Driven Pegmatite Exploration Targeting in a Geologically Underexplored Area in the Tysfjord Region, Norway

We compute probabilistic Niobium–Yttrium–Fluorine (NYF) pegmatite prospectivity maps in the Tysfjord region in Northern Norway. NYF pegmatites are generally enriched in rare earth minerals and represent residual melts derived from granitic plutons or melts formed by partial melting of metaigneous rocks. In Tysfjord, however, these pegmatites contain high‐purity quartz, which is the major target commodity of exploration and mining. As the area is geologically underexplored, we employ a data analytics approach for the discovery of new deposits. We carefully lay out our knowledge base and how it impacts the working hypothesis and feature engineering. Self‐organizing maps are employed as an unsupervised and random forest classification as a supervised data analytics algorithm to process and link features derived from airborne magnetic and radiometric maps with sparse pegmatite occurrences available in the form of outcrops and active and abandoned mines. The predictive power of our probabilistic pegmatite prospectivity maps is analysed by means of additional boreholes, which indicates the usefulness of our prospectivity maps for exploration targeting. We recommend employing unsupervised and supervised data analytics approaches in exploration targeting case studies where uncertainty about the predictive power of the available database cannot be ruled out before subjecting the database to data analytics.