Borehole Logging Lithological Classification in Carbonatite‐Hosted Niobium Deposit Integrating Geophysical and Geochemical Data With Self‐Organizing Maps

Lithological classification from borehole data remains challenging in geologically complex alkaline–carbonatite systems, where compositional overlap and gradual transitions between lithotypes limit conventional interpretation methods. This study presents an integrated and automated workflow for drill‐core lithological classification using self‐organizing maps (SOMs), an unsupervised artificial neural network, applied to a carbonatite‐hosted niobium deposit. The approach integrates 17 variables, including major‐element geochemistry (e.g., Nb 2 O 5 , Fe 2 O 3 , P 2 O 5 and SiO 2 ), physical properties (bulk density) and downhole geophysical logs (gamma radiation, magnetic susceptibility, resistivity and neutron porosity), enabling multivariate analysis of more than 40,000 drill‐core samples. Principal component analysis (PCA) highlighted robust associations between mineralization‐related variables, whereas inverse correlations of SiO 2 , K 2 O and resistivity enhanced host‐rock discrimination. SOM modelling defined five clusters validated against reference lithologies, including nelsonites, carbonatite breccias, calcitic and barite‐rich carbonatites and host rocks. The model accurately classified more than 95% of nelsonite and barite‐rich carbonatite samples, with errors below ±5% for key variables such as Nb 2 O 5 and Fe 2 O 3 , whereas partial overlap between breccias and host rocks reflected natural geological transitions. Overall, the results demonstrate that SOM‐based unsupervised learning provides a robust, objective and reproducible framework for lithological domain definition, offering strong potential to enhance geological interpretation, exploration targeting and geometallurgical modelling in complex ore systems.