Geometallurgical modeling is pivotal for optimizing mining projects, yet the selection of an appropriate modeling strategy often relies on empirical experience rather than a systematic methodology. This paper introduces a novel systems-theoretic framework that formalizes geometallurgical modeling as an information acquisition problem under cost and uncertainty constraints. We propose a taxonomy of four fundamental strategies (S0–S3) defined by their use of direct measurement, interpolation, and regression to populate the key target variable geometallurgical ore type in a spatial block model. A generalized decision algorithm is developed to select the optimal strategy by evaluating economic feasibility and predictive accuracy against system characteristics such as deposit complexity, cost structure, and internal variable correlations. The framework demonstrates that the proxy-based strategy (S2) generally offers the most robust balance between cost and accuracy for complex deposits. This work provides a scalable and generalizable approach applicable not only to geometallurgy but also to other domains involving spatial resource characterization under uncertainty, such as environmental monitoring and petroleum engineering.
Kalashnikov et al. (Mon,) studied this question.