Geometallurgy is an important addition to any evaluation project or mining operation. As an integrated approach, it establishes 3D models which enable the optimisation of net present value and effective orebody management, while minimising technical and operational risk to ultimately provide more resilient operations. Critically, through spatial identification of variability, it allows the development of strategies to mitigate the risks related to variability (e.g., collect additional data, revise the mine plan, adapt or change the process strategy, or engineer flexibility into the system). Geometallurgy promotes sustainable development when all stages of extraction are performed in an optimal manner from a technical, environmental, and social perspective. To achieve these goals, development of innovative technologies and approaches along the entire mine value chain are being established. Geometallurgy has been shown to intensify collaboration among operational stakeholders, creating an environment for sharing orebody knowledge and improving data acquisition and interpretation, leading to the integration of such data and knowledge into mine planning and scheduling. These aspects create better business optimisation and utilisation of staff, and lead to operations that are more resilient to both technical and non-technical variability. Geometallurgy encompasses activities that utilise improved understanding of the properties of ore and waste, which impact positively or negatively on the value of the product, concentrate, or metal. Properties not only include those that impact on processing efficiency, but also those of materials which will impact on other actions such as blasting and waste management. Companies that embrace the geometallurgical approach will benefit from increased net present value and shareholder value.

When developing a process flowsheet, the risks in achieving positive financial outcomes are minimised by ensuring representative metallurgical samples and high quality testwork. The quality and type of samples used are as important as the testwork itself. The key characteristic required of any set of samples is that they represent a given domain and quantify its variability. There are those who think that stating a sample(s) is representative makes it representative without justification. There is a need to consider both (1) in-situ and (2) testwork sub-sample representativity. Early ore/waste characterisation and domain definition are required, so that sampling and testwork protocols can be designed to suit the style of mineralisation in question. The Theory of Sampling (TOS) provides an insight into the causes and magnitude of errors that may occur during the sampling of particulate materials (e.g., broken rock) and is wholly applicable to metallurgical sampling. Quality assurance/quality control (QAQC) is critical throughout all programmes. Metallurgical sampling and testwork should be fully integrated into geometallurgical studies. Traditional metallurgical testwork is critical for plant design and is an inherent part of geometallurgy. In a geometallurgical study, multiple spatially distributed small-scale tests are used as proxies for process parameters. These will be validated against traditional testwork results. This paper focusses on sampling and testwork for gold recovery determination. It aims to provide the reader with the background to move towards the design, implementation and reporting of representative and fit-for-purpose sampling and testwork programmes. While the paper does not intend to provide a definitive commentary, it critically assesses the hard-rock sampling methods used and their optimal collection and preparation. The need for representative sampling and quality testwork to avoid financial and intangible losses is emphasised.

Sheeted vein gold deposits are often characterised by multiple sub-parallel veins and free-milling coarse gold. Inherent mineralisation heterogeneity results in grade and process parameter variability, which increases project risk if not quantified. Measured grade variability is often exacerbated by poorly designed sampling and testwork protocols. Protocols that are optimised within the framework of the Theory of Sampling (TOS) to suit the ore type, together with quality assurance/quality control systems, will reduce variability and provide fit-for-purpose results. Geometallurgy can be broadly split into two key approaches: strategic and tactical (or operational). The strategic approach focuses on the whole orebody and long-term life-of-mine view, whereas tactical geometallurgy relates to a more short- to medium-term view during mining. The geometallurgical approach requires spatially distributed samples within a deposit to support variability modelling. Diverse attributes from core logging, mineralogical/textural determination and small-scale tests are used to measure variability. This contribution presents a case study that emphasises an early-stage strategic geometallurgical programme applied to a gravity recoverable gold (GRG) dominated deposit. It exemplifies how data can be acquired from a well-designed and planned programme to support resource estimation, a pre-feasibility study, trial mining and fast-track to production. A tactical geometallurgical programme is embedded into the mine operation.

Grade control in underground mines aims to deliver quality tonnes to the process plant via the accurate definition of ore and waste. It comprises a decision-making process including data collection and interpretation; local estimation; development and mining supervision; ore and waste destination tracking; and stockpile management. The foundation of any grade control programme is that of high-quality samples collected in a geological context. The requirement for quality samples has long been recognised, where they should be representative and fit-for-purpose. Once a sampling error is introduced, it propagates through all subsequent processes contributing to data uncertainty, which leads to poor decisions and financial loss. Proper application of the Theory of Sampling reduces errors during sample collection, preparation, and assaying. To achieve quality, sampling techniques must minimise delimitation, extraction, and preparation errors. Underground sampling methods include linear (chip and channel), grab (broken rock), and drill-based samples. Grade control staff should be well-trained and motivated, and operating staff should understand the critical need for grade control. Sampling must always be undertaken with a strong focus on safety and alternatives sought if the risk to humans is high. A quality control/quality assurance programme must be implemented, particularly when samples contribute to a reserve estimate. This paper assesses grade control sampling with emphasis on underground gold operations and presents recommendations for optimal practice through the application of the Theory of Sampling.

The mining industry routinely collects samples to assist with decision making, whether for exploration, resource estimation, grade control, or plant design and balances. Poorly designed sampling protocols can result in elevated project risk by increasing variability. Critically, such variability produces both financial and intangible losses. Sample collection, preparation and assay or test work protocols that are optimised to suit the ore type, together with QAQC systems will reduce variability. Many gold deposits display a high natural variability, where the in situ variability can be enhanced by poor sampling practice to yield a high-nugget effect. In this case, specialised protocols are often required. Reporting codes require the Competent Person to consider the quality and implication of sampling programmes. Despite its importance, sampling often does not receive the attention it deserves. The importance of good sampling practice is exemplified through a series of case studies, which show the many sampling issues that frequently go unrecognised or unaddressed, resulting in poor decisions and financial loss.

Determination of the sampling constant is an important step when applying the Fundamental Sampling Error equation to optimise sampling protocols. The classic method for its determination is based on the heterogeneity test (HT). In coarse gold-dominated mineralisation, the HT sometimes provides an evaluation of the fine-gold background grade population heterogeneity, but understates that of the important high-grade coarse-gold component. This is because the total mass of fragments is too small to represent the full gold particle size distribution of the mineralisation. Despite this, single heterogeneity tests (SHT) are often undertaken on deposits where the presence of coarse gold is ignored, not realised or understated. Resultant sampling and assaying protocols are frequently of poor quality and do not match the mineralisation characteristics. For coarse gold-dominated mineralisation, an empirical approach for sampling constant determination is recommended via direct estimation of the liberation diameter.