3D HS orebody delineation integrating CatBoost modelling and Transformer-based evidence fusion: A case study from Čukaru Peki, Serbia

High-sulfidation (HS) orebodies are typically characterised by advanced argillic alteration and strong structural control. However, their 3D delineation remains challenging due to the inherent complexity of alteration facies and the limitations of discrete drillhole observations. We present a comprehensive 3D machine-learning workflow to delineate HS orebodies at the Čukaru Peki deposit (Eastern Serbia) by integrating drill-core SWIR spectroscopy with geological and geochemical constraints.Alteration mineralogy was characterised from SWIR spectra using The Spectral Geologist (TSG), extracting diagnostic sulfate–clay signatures (e.g., alunite-group minerals) and spectral scalars (e.g., ~2.20 μm absorption depth and white-mica crystallinity). To bridge the gap between discrete samples and a continuous volume, we constructed voxel-scale attribute fields using CatBoost regression. Unlike conventional distance-based interpolation, CatBoost learns nonlinear spatial dependencies conditioned on coordinates and geological context (lithology, alteration facies, and fault proximity), enabling data-driven 3D inference across the entire modelling volume.Subsequently, a Transformer encoder was employed for voxel-wise evidence fusion on the stacked 3D attribute layers. The model captures the nonlinear mapping of "multi-evidence interaction → HS mineralisation probability" to output a probabilistic targeting volume. The model was trained on labelled exploration drilling data (604 samples) and rigorously validated against an independent in-mine dataset (2,850 samples). Performance evaluation using confusion matrices and ROC curves consistently suggests that sulfur enrichment, alteration intensity, and structural proximity jointly govern HS distribution. This approach provides a robust, interpretable basis for 3D orebody modelling and drill targeting in complex porphyry–epithermal systems.