Review of gold reef sampling and its impact on the mine call factor

Fourie, Andries; Minnitt, R.C.A.

doi:10.17159/2411-9717/2016/v116n11a2

Cited by 5 publications

(7 citation statements)

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“…The greatest EE during face sampling arises from differential, hard versus soft rock properties, as a greater quantity of softer material usually enters the sampling bag (Fourie and Minnitt, 2016). If this is high-grade, then the sample will be positively biased, and if low-grade, vice versa.…”

Section: Sample Collection and Methodologymentioning

confidence: 99%

“…In a study based on Witwatersrand (RSA) gold mines, Fourie and Minnitt [21] identified an "in-stope sample discard process", where samplers were prone to make a selection of what they believe was better looking material. Such discard of waste and preferential selection of mineralised material leads to a positive "waste discard bias" and hence higher sample assays [21].…”

Section: Sample Collection and Methodologymentioning

confidence: 99%

“…Its application to underground grade control sampling has been variable, with leading use in the South African gold sector [19][20][21]. Some of the resistance to the application of TOS relates to the use of the Fundamental Sampling Error (FSE) equation and confusion as to its calibration and application.…”

Section: Ae -Analytical Processmentioning

confidence: 99%

“…Theory of Sampling strictly does not include sampling of in-situ material such as face samples, as the sample does not have an equiprobable chance of being collected. However, its application is relevant and helps to understand some of the challenges of field sampling through analysis of the so-called incorrect sampling errors during the sample collection process [4,21]. The application of TOS includes the mandatory use of an appropriate QAQC system, including the use of duplicates.…”

Section: Ae -Analytical Processmentioning

confidence: 99%

“…Similarly, there can also be a relationship between mine size and sample quality, where smaller mines collect higher quality samples. However, some large mines, notably those on the Witwatersrand, have put much time and effort into improving sample quality [19][20][21]. Mass and quality issues relate to: the rate of face advance, time pressures, and availability of the geologist.…”

Section: Sampling Under Production Conditionsmentioning

confidence: 99%

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Integrating the Theory of Sampling into Underground Mine Grade Control Strategies

et al. 2018

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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.

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Section: Sample Collection and Methodologymentioning

confidence: 99%

Section: Sample Collection and Methodologymentioning

confidence: 99%

Section: Ae -Analytical Processmentioning

confidence: 99%

Section: Ae -Analytical Processmentioning

confidence: 99%

Section: Sampling Under Production Conditionsmentioning

confidence: 99%

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Integrating the Theory of Sampling into Underground Mine Grade Control Strategies

et al. 2018

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Machine Learning-Based Delineation of Geodomain Boundaries: A Proof-of-Concept Study Using Data from the Witwatersrand Goldfields

et al. 2023

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Machine-aided geological interpretation provides an opportunity for rapid and data-driven decision-making. In disciplines such as geostatistics, the integration of machine learning has the potential to improve the reliability of mineral resources and ore reserve estimates. In this study, inspired by existing geostatistical approaches that use radial basis functions to delineate domain boundaries, we reformulate the problem into a machine learning task for automated domain boundary delineation to partition the orebody. We use an actual dataset from an operating mine (Driefontein gold mine, Witwatersrand Basin in South Africa) to showcase our new method. Using various machine learning algorithms, domain boundaries were created. We show that based on a combination of in-discipline requirements and heuristic reasoning, some algorithms/models may be more desirable than others, beyond merely cross-validation performance metrics. In particular, the support vector machine algorithm yielded simple (low boundary complexity) but geologically realistic and feasible domain boundaries. In addition to the empirical results, the support vector machine algorithm is also functionally the most resemblant of current approaches that makes use of radial basis functions. The delineated domains were subsequently used to demonstrate the effectiveness of domain delineation by comparing domain-based estimation versus non-domain-based estimation using an identical automated workflow. Analysis of estimation results indicate that domain-based estimation is more likely to result in better metal reconciliation as compared with non-domained based estimation. Through the adoption of the machine learning framework, we realized several benefits including: uncertainty quantification; domain boundary complexity tuning; automation; dynamic updates of models using new data; and simple integration with existing machine learning-based workflows.

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Where is all the gold?

Handley¹

2023

J. S. Afr. Inst. Min. Metall.

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The Witwatersrand Basin contains by far the most gold ever found, and has hosted mining from its discovery in 1886 to the present. For many years, South Africa was the world's largest producer of gold, nearly all of which came from the Witwatersrand. Since 2000, South Africa has fallen back several positions because of declining gold output. There are many complex and varied reasons for this; however, declining gold resources in the Witwatersrand Basin are not one of them. As far as the author knows, there are no qualified estimates in the literature of gold remaining in the Witwatersrand Basin. This paper collects mining data from several sources and then, using elementary gold accounting and error analysis, attempts to estimate the amount of gold remaining in the Witwatersrand Basin. It includes gold underground in existing and defunct mines, in evaluated ore resources, and dumps. Compiled data from 1887 to 2019 shows that 50 200 t of gold were produced by Witwatersrand mines, while it is estimated that a further 48 100 t remain underground, both within and outside of mine leases, a further 1 600 t on the surface in tailings, and an unknown amount in rock dumps. Nearly all of this gold will remain inaccessible with current mining methods, and major technical developments in mining will be necessary before any of the gold can be categorized as a code-compliant resource or reserve. To win this prize, the mining industry will have to rethink its approach to mining, both in old mining leases and in the unexploited ground. It will also have to find effective means of preventing gold theft and informal mining, which are on the rise.

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Review of gold reef sampling and its impact on the mine call factor

Cited by 5 publications

References 1 publication

Integrating the Theory of Sampling into Underground Mine Grade Control Strategies

Integrating the Theory of Sampling into Underground Mine Grade Control Strategies

Machine Learning-Based Delineation of Geodomain Boundaries: A Proof-of-Concept Study Using Data from the Witwatersrand Goldfields

Where is all the gold?

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