3733 CHALLENGES OF EXTREME GRADES IN RESOURCE ESTIMATION: SPATIAL CAPPING AS AN ALTERNATIVE TO DOMAIN CAPPING OF OUTLIERS
CHALLENGES OF EXTREME GRADES IN RESOURCE ESTIMATION
DOI:
https://doi.org/10.17159/Abstract
Accurate ore grade estimation is essential for mine planning and economic evaluation, particularly in deep-level gold mines where extreme grade values (outliers) may distort resource assessments. Traditional capping methods, which apply a uniform threshold to outliers, often fail to account for spatial and geological variability, leading to biased estimates. This study introduces a localized, distance-weighted capping approach that refines outlier treatment by incorporating spatial relationships and geological context within the kriging search ellipse. The proposed method transitions from one-dimensional point estimation to two-dimensional block estimation, to enhance accuracy in Witwatersrand-type deposits. A high-resolution simulation generates a synthetic "ground truth" orebody, subsampled to mimic real-world data scarcity. A three-parameter lognormal distribution models grade variability, with capping values optimized by minimizing the mean squared error (MSE) between estimated and simulated block grades. Comparative analyses against traditional capping techniques demonstrate that the dynamic method improves regression fit, reduces conditional bias, and aligns more closely with production reconciliation data. Validation using swath plots, QQ-plots, and mine call factor (MCF) comparisons confirms that the spatially adaptive capping technique mitigates over- and under-estimation, particularly in high-grade zones. Results indicate superior performance over conventional domain capping, with higher R² values and better alignment with actual production metrics. This approach offers a reproducible, scientifically rigorous framework for resource estimation, reducing reliance on rigid sub-domaining while enhancing decision-making in complex ore bodies. The study underscores the importance of integrating geological information with advanced geostatistics to optimize grade estimation and mine planning.
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Copyright (c) 2026 Richard Charles Anson Minnitt, Dries, Peter
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