3D geostatistical modelling of a tailings storage facility: Resource potential and environmental implications

Abstract

The management of mine tailings presents a global challenge. Re-mining of tailings to recover remaining metals and other valuable constituents could play a crucial role in reducing the volume of stored tailings. To assess the resource potential of tailings storage facilities, 3D resource models must be constructed. This is not straightforward owing to the heterogeneous nature of tailings. In this case study, modelling of the Davidschacht tailings deposit was performed using universal sequential Gaussian simulation, to account for the strong trends and heterogeneity in chemical distribution. The tonnages of the valuable elements were estimated with reasonable certainty, confirming that relatively few drill holes are required for robust resource estimates of tailings storage facilities. Zinc is the most abundant valuable metal (∼5,170±517 t), followed by Pb (∼2,060±206 t), Cu (∼550±66 t) and In (∼11±1 t), while the toxic elements As and Cd are present in tonnages of ∼8,350±585 t and ∼47±4 t, respectively, with errors given for 95 % confidence levels. Despite the In tonnage being low compared to the other elements, its in situ value is around half that of Cu and Pb, demonstrating the importance of high value by-products for re-mining potential. Although tailings deposits typically have lower grades than primary ore deposits, and the quantities of recoverable valuable elements may be even lower due to current technological limitations, re-mining of TSFs may help to diversify raw materials supply chains and should also be considered for rehabilitation purposes. Geostatistical modelling, particularly universal kriging-based simulation, has been proven to produce robust tonnage estimates of tailings storage facilities and should be adopted in industry to reduce the technical and financial uncertainties associated with re-mining.