Quality and sampling error quantification for gold mineral resource estimation

Sampling is a vital component during all stages of the mine value chain. It includes the sampling of in situ material and broken rock for geological, metallurgical and geoenvironmental purposes. Sampling errors are defined in the context of the Theory of Sampling (TOS), where incorrect actions may lead to uncertainty and create a significant overall sampling + measurement error. The TOS breaks down this error into a series of contributions along the full value chain (the planning to assay-measurement process). Errors are additive throughout this pathway, unavoidably exacerbating risk. After collection, sampling errors also occur throughout all subsequent downstream processes contributing to uncertainty in test work and any decisions made thereon. Across the full mine value chain, the sum of these errors generate both financial and intangible losses. In essence, poorquality, non-representative sampling increases project risk and may consequently often lead to incorrect project valuation. There is hardly any other application field where this is as critically important than for Gold mineral resource estimation, because of the very low grades and the extremely irregular mineralisation heterogeneities encountered (Figure 1). Sampling—the first critical success factor in the mine value chain The data produced must be fit-forpurpose to contribute to mineral resources/ore reserves reported in accordance with the 2017 PERC or other international codes. Quality assurance/quality control (QA/QC) is critical to maintaining data integrity through documented procedures, sample security, and monitoring of precision, accuracy and contamination. Samples and their associated assays are key inputs into important decisions throughout the mine value chain. The TOS was first developed in the 1950s by Dr Pierre Gy to deal with sampling challenges in the mining industry, though it has far wider applications Saranchimeg Purevgerel Simon Dominy