Quantifying uncertainty and improving prospectivity mapping in mineral belts using transfer learning and Random Forest: A case study of copper mineralization in the Superior Craton Province, Quebec, Canada

Mineral prospectivity mapping (MPM) involves identifying locations with a higher potential for mineral exploration based on a set of explanatory variables. In cases where there is a scarcity or absence of unfavorable sites that adequately represent the geological context for deposit discovery, generating synthetic negative data sets becomes necessary to employ a machine learning algorithm optimally. Moreover, when favorable sites are insufficient for deposit discovery within a geological zone, machine learning methods can potentially result in large and highly uncertain prospecting areas. This article proposed a concept based on transfer learning by applying the knowledge gained from mineral belt signatures in different geological zones to a related area. The positive training data were taken from five mineral belts distanced from each other, while the negative data were sampled using geological constraints based on the distance to occurrences and spatial associativity. The results demonstrate that transfer learning, combined with geological constraints applied to the creation of negative datasets, improves model performance and prediction of known deposits while significantly reducing uncertainties. Mineral prospectivity models for predicting potential copper formations were generated using data from the Quebec Government's spatial reference geomining information system, SIGEOM. The case study for this work focused on the geological province of the Superior Craton, which encompasses the vast majority of northeastern Quebec.