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The classification of mineral resources is crucial for public disclosure and is used to evaluate the risk associated with the mineral deposit, enabling informed decisions. To address this need, this study proposes the use of a multi-layer scorecard workflow (MLSW) for mineral resource classification that considers multiple factors from different disciplines. This approach is highly flexible as the competent user may adapt the scorecard workflow to the particularities of each deposit. In this paper, we considered classical metrics for resource classification, such as the number of samples, the slope of regression, kriging efficiency, and kriging variance, combinedwith more modern ones (Risk Index), which contemplates the combination of the estimation error, and geological continuity by a probabilistic approach. The methodology can also incorporate qualitative information such as the geological complexity. The proposed workflow has been applied in two different databases, demonstrating its transparency, auditability, and applicability.

Practical application of a multi-layer scorecard workflow (MLSW) for comprehensive mineral resource classification