Akademik Veri Yönetim Sistemi
MINERALS, sa.4, 2025 (SCI-Expanded, Scopus)
Machine learning (ML) is increasingly applied in earth sciences, including in mineral resource estimation. A critical step in this process is domaining, which significantly impacts estimation quality. However, the importance of domaining within ML-based resource estimation remains under-researched. This study aims to directly assess the effect of domaining on ML estimation accuracy. A copper deposit with well-defined, hard-boundary, low- and high-grade domains was used as a case study. Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and ensemble learning were employed to estimate copper distribution, both with and without domaining. Estimation performance was evaluated using summary statistics, swath plot analyses, and the quantification of out-of-range blocks. The results demonstrated that estimations without domaining exhibited substantial errors, with approximately 30% of blocks in the high-grade domain displaying values outside their expected range. These findings confirm that, analogous to classical methods, domaining is essential for accurate mineral resource estimation using ML algorithms.