Classification of Four Igneous Host-Rock Types Using Apatite Trace-Element Geochemistry: Source-Aware Evaluation of Deep Learning and Geochemical Representations

Apatite rare-earth-element (REE) compositions can discriminate igneous host-rock types, but systematic comparisons of deep-learning models and alternative REE representations remain limited, while analyses from shared publications or geological bodies complicate estimates of generalization. We evaluated four-class classification in a cohort of 4148 apatite analyses from 76 connected source groups. Six primary models (SVM, RF, XGBoost, MLP, ResNet, and FT-Transformer) were compared across seven representations derived from the same 14 REEs. Logistic regression and linear discriminant analysis served as baselines, and ILR features were examined separately. Source-group-aware nested cross-validation used five outer and three inner folds, with candidate selection and tuning confined to outer training groups. The selected procedure achieved a five-seed pooled out-of-fold Macro-F1 of 0.556 (95% source-group bootstrap CI, 0.398–0.652). With Raw inputs, logistic regression achieved 0.573 (0.436–0.672). Confidence intervals overlap, and paired comparisons after multiplicity adjustment do not support a reliable ranking of the candidates or a consistent advantage for deeper architectures or combined representations. Raw, Geo, CLR, and ILR are alternative representations of the same REE measurements rather than independent chemical information. Logistic regression using Raw inputs remained competitive, while transfer to unseen geological sources remains uncertain, especially for classes represented by few independent sources.