Mineral Resource Disclosure with Probabilistic Models

Abstract Geological variability at all scales and sparse sampling make Mineral Resource uncertainty an inevitable reality for mining companies and the concerned public. The workflow for probabilistic resource modeling is established, providing accurate and precise estimates of uncertainty in local and global resources. Companies that adopt standard probabilistic resource modeling practices across assets have powerful information for risk-qualified asset management and consistent decisions. It is natural to consider the use of probabilistic models for the disclosure of resources, although this does not match historical and current practice. Regulatory adoption of probabilistic resource reporting standards is a worthy end goal, but the application of probabilistic models for disclosing resources within the current deterministic disclosure framework may be an incremental and necessary first step, which itself provides immediate advantages. Compared against conventional estimation methods, simulated realizations offer superior features for input to resource estimates, including realistic variability and estimates at relevant scales. The probabilistic models contribute to informed decision-making around measured, indicated, and inferred classifications. The general framework for disclosing resources with probabilistic models is outlined and demonstrated. Practical considerations include the modeling, checking, and auditing of probabilistic model-based resources, comparing them with legacy resources, using them as input to mine planning, and transfer to mining reserves.