Geological mapping of copper deposits in the democratic republic of Congo through remote sensing data and machine learning

IntroductionThis study aimed to identify hydrothermal alteration zones and structural features associated with copper mineralisation in the Musonoi region, Lualaba Province, Democratic Republic of the Congo (DRC), within the Central African Copperbelt, using remote sensing and machine learning (ML). The study responds to the need for cost-effective and scalable exploration approaches in structurally complex tropical terrains.MethodsMultispectral satellite data from ASTER and Landsat 8 OLI, integrated with field observations and borehole information, supported the development of a predictive model. Principal Component Analysis (PCA), band ratios, and both manual and automated lineament extraction were used to enhance spectral and structural features. A lineament density map and hydrothermal alteration indices were produced and integrated with geological field data to verify the relationship between surface anomalies and subsurface mineralisation.ResultsRandom Forest classification indicated strong mineralisation in zones with high lineament density, fault intersections, and chlorite and kaolinite alteration. The main controlling variables were lineament density at 33.6%, fault proximity at 31.0%, and hydrothermal alteration index at 26.3%. Siliceous laminated rocks and basal dolomitic shale hosts mineralised units. Field validation confrmed that the model reflects known deposits, showing the strength of remote sensing and machine learning for exploration in complex tropical terrains.DiscussionThe study highlights the novelty of integrating Random Forest with multi source geospatial information in a structurally complex tropical terrain, and shows that this approach provides a cost effective and scalable tool for exploration in the Central African Copperbelt and similar geological provinces. Limitations related to spatial resolution and training data coverage remain and should be addressed in future work.