A mining sample is a small, manageable, and ideally representative portion of a larger geological entity, such as an ore body or mineral deposit, systematically collected to infer the characteristics of the whole (OSME, 2018). Its fundamental importance permeates the entire mining value chain, influencing decisions from initial exploration through resource definition, economic feasibility assessment, operational grade control, and even environmental monitoring (Dominy et al., 2018).
The objectives of sampling are multifaceted: in exploration, it unveils mineral potential and guides discovery for resource definition (EarthSci, n.d.). It quantifies ore grade, tonnage, and geometry; it informs investment and development decisions for economic viability during operations; it optimizes extraction through grade control (Core Case, n.d.b) and in geometallurgy, it predicts material behavior and recovery.
A variety of sample types exist, including grab, chip, channel, drill core, and bulk samples, each suited to specific stages and geological conditions (OSME, 2018).
To ensure data integrity, the Theory of Sampling (TOS) provides a framework for minimizing inherent variabilities and errors (Dominy et al., 2018). This is complemented by rigorous Quality Assurance/Quality Control (QA/QC) programs, which utilize standards, blanks, and duplicates to monitor and validate the sampling and analytical processes (Rangefront, n.d.a). Ultimately, rigorous and scientifically sound sampling is indispensable for accurate resource evaluation and sustainable mineral development.
Why do you think it’s crucial to maintain strict quality control when collecting and analyzing mining samples?
