Performance Comparison of Some Artificial Intelligence Algorithms for Metallic Mineral Deposits: A Case from Türkiye

Recent advances in artificial intelligence and geospatial data analytics have led to an increasing adoption of data-driven approaches in the identification and prediction of mineral deposits. Traditional mineral exploration methods often rely on single data sources or expert-driven interpretations and may therefore be inadequate in regions where geological information is limited or spatially complex. In contrast, artificial intelligence–based approaches enable the quantitative assessment of mineral potential and the identification of spatial patterns associated with mineralization by jointly integrating multi-source geological, geophysical, and remote sensing data. Therefore, the comparative evaluation of different artificial intelligence algorithms using approaches that account for spatial dependence is critical for selecting reliable and interpretable models in early-stage mineral exploration conducted under data-limited conditions.

This study focuses on a comparative evaluation of artificial intelligence algorithms for predicting potential iron (Fe) mineralization under limited geological data conditions in a region with metallic mineralization potential in Türkiye. The study area covers approximately 2,340 km². A total of seven predictor variables were incorporated into the modeling, classified into geological (lithology, geological age, formation type), structural (fault density), geophysical (magnetic anomaly and gravity-tilt features), and remote sensing–based datasets (iron oxide potantial zones derived from ASTER imagery). The mineralization inventory is highly sparse, comprising only 15 iron occurrences and 24 non-iron reference points selected by geologists To address this limitation, a spatially aware hard negative mining strategy was applied, in which negative samples were preferentially selected from areas spatially proximal to known mineralization occurrences. Model performance was evaluated using GroupKFold-based spatial cross-validation to minimize bias arising from spatial autocorrelation, within which the Random Forest (RF) and XGBoost (XGB) algorithms were compared. The obtained results show that the RF and XGB models achieved mean Area Under Curve (AUC) values of 0.85 and 0.89, respectively. According to the generated mineral prospectivity maps, the Random Forest model delineates approximately 207.02 km² of high-potential areas (probability ≥ 0.90), while the XGBoost model identifies high-potential areas covering approximately 404.04 km² at the same probability threshold. These results indicate that there are pronounced differences in the spatial distribution of high-potential areas depending on the algorithm used. Additionally, the feature importance analysis revealed that geological age, magnetic anomaly, formation type, and gravity-tilt features are the primary controlling factors influencing the spatial distribution of iron mineralization.

This study outcomes revealed the importance of algorithm selection and spatially aware validation strategies in artificial intelligence–based mineral exploration. The findings indicate that reliable mineral prospectivity assessments can be achieved even under limited geological data conditions. Furthermore, in early-stage exploration programs, these approaches strengthen effective target area prioritization and decision-support processes and contribute to cost reduction through more efficient planning of exploration activities.