Effectiveness of ore grade control at operating mines depends on both the quality and quantity of the samples used. Therefore, optimisation of grade control procedures requires analysis of sample quality and their spatial distribution. This approach, implying quantitative estimation of both these factors and quantification of their contribution to the grade control errors, was used for comparing two different grade control procedures at the Yandicoogina (Yandi) iron-ore open pit mine, located in the eastern part of the Pilbara region of Western Australia. At Yandi, pisolitic iron oxide mineralisation is distributed within a meandering palaeoriver channel, characterised by abundant clay pods that contain the deleterious components; in particular Al2O3 and SiO2.

The sole reason for a mine to exist is to extract the finite mineral resource. Accurate grade control is critical to the economics of any mine. If grade control is not optimised, then no matter how good the downstream processes are, the full potential of the operation will not be realised. The detailed implementation of grade control varies but typically consists of sampling and assaying to determine the location of the ore zones. A lot of time, money and effort are spent defining the location of the ore as accurately as possible … but then it is blown up! The effect that blasting has on grade control is rarely adequately accounted for when the rock is excavated because there has never been an accurate and practical method for measuring blast movement.

During core drilling, runs of core up to about three metres long are extracted from the core barrel. The extraction process rotates the core randomly, so that once the core is laid out in core boxes its original orientation is lost, although the orientation of the core axis is generally known. Various down-hole surveying techniques are available for this, and the common usage of 3-D modelling software has lead to holes being generally very well surveyed. <...>

Монография Австралазийского института горного дела и металлургии посвящена описанию и обобщению самого передового опыта в области оценки минеральных ресурсов и рудных запасов. Настоящее издание состоит из 9 глав и 79 статей, написанных известными в мире учеными и практиками, в которых рассматривается широчайший спектр вопросов, касающихся в той или иной степени очень сложной и актуальной для любой горной компании проблемы оценки ресурсов и запасов. В книге можно найти много полезных сведений о новациях, иногда коренным образом отличающихся от российской зарегламентированной действительности в этой сфере. Книга будет полезна специалистам, работающим в геологической и горнодобывающей отраслях, а также аспирантам и студентам горных вузов.

What is the quality of the sample and assay data? Am I confident enough in my data to be able to make a potentially costly decision? These are the types of questions which should be asked when dealing with assay data.

This document gives guidelines for taking duplicate measurements by in-situ analytical instruments, or collecting duplicate samples for analysis in the mobile field laboratory, to assess random errors originating from sampling and analytical procedures, and to estimate the uncertainty of measurements. Since, NORISC is dealing with the assessment of contamination of small-size areas within cities, and relies mainly on in-situ analytical methods, a cost-effective technique, using robust analysis of variance for the estimation of necessary quality control parameters, and measurement uncertainty, is explained with examples <...>

Resource modelling is a complex process involving different specialists with relevant experience using a multi-disciplinary approach and the best available technology and reviews by independent auditors. The reliability of the final resource estimate is highly dependent on the quality control exercised at each stage of the process. At each step in the resource modelling process it is necessary to define the specific objectives, the methodology proposed to achieve those objectives and to establish a set of checks and validation tools to assess the effectiveness of the proposed methodology. Designation of responsibility and authority for meeting these objectives must also be clearly identified. External audits must also be incorporated to review and validate the implementation of new procedures.

Resource modelling is the basis for any economic appraisal of a mining project and includes a number of steps from data acquisition and validation to resource reporting, classification, and risk analysis

Control of analytical data quality is usually referred to in the mining industry as Quality Assurance and Quality Control (QAQC), and involves the monitoring of sample quality and quantification of analytical accuracy and precision. QAQC procedures normally involve using sample duplicates and specially prepared standards whose grade is known. Numerous case studies indicate that reliable control of sample precision is achieved by using approximately 5% to 10% of field duplicates and 3% to 5% of pulp duplicates. These duplicate samples should be prepared and analyzed in the primary laboratory.

Vallée (1998) indicated that few exploration and mining companies have explicit and systematic quality-assurance policies, and identified three main approaches: laissez-fair, catch-as-catch-can, and systematic quality control, the latter being very uncommon. In the author’s experience, this situation has not significantly improved in the intervening twelve years.