Current state of industry practice in mineral resource estimation and classification

Research Article| July 01, 2001 Classification and Reporting of Mineral Resources for High-Nugget Effect Gold Vein Deposits SIMON C. DOMINY; SIMON C. DOMINY Economic Geology Research Unit, School of Earth Sciences, James Cook University, Townsville, Queensland, Australia Search for other works by this author on: GSW Google Scholar PATRICK R. STEPHENSON; PATRICK R. STEPHENSON Australian Mining Consultants Pty Ltd. Melbourne, Victoria, Australia Search for other works by this author on: GSW Google Scholar ALWYN E. ANNELS ALWYN E. ANNELS SRK Consulting (UK) Ltd. Cardiff, Wales, United Kingdom Search for other works by this author on: GSW Google Scholar Exploration and Mining Geology (2001) 10 (3): 215–233. https://doi.org/10.2113/0100215 Article history received: 29 Mar 2001 accepted: 09 Dec 2002 first online: 02 Mar 2017 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Search Site Citation SIMON C. DOMINY, PATRICK R. STEPHENSON, ALWYN E. ANNELS; Classification and Reporting of Mineral Resources for High-Nugget Effect Gold Vein Deposits. Exploration and Mining Geology 2001;; 10 (3): 215–233. doi: https://doi.org/10.2113/0100215 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu nav search search input Search input auto suggest search filter All ContentBy SocietyExploration and Mining Geology Search Advanced Search Abstract The often complex, erratic, and localized nature of gold is a common feature of many vein-style gold deposits. This style of mineralization is often referred to as being nuggety or possessing a high-nugget effect. As a result of these complexities resource estimation is difficult and in general, only Exploration Results can be provided or an Inferred Mineral Resource estimated from surface drilling data alone. Underground development, further drilling, and probably bulk sampling will be required to delineate Indicated and Measured Resources. Tonnages can generally be estimated from diamond drill and development information with a reasonable degree of confidence. Grade is much more difficult to define with confidence because it is commonly highly erratic and discontinuous in nature. The dependency of higher confidence Resource categories on development information may create a Catch 22 situation, with funding for such development often depending on the prior definition of at least Indicated Resources. There are no easy solutions to these challenges posed by high-nugget effect deposits, and it is important when classifying and reporting not to downplay the uncertainties often associated with Mineral Resource and Ore Reserve estimates for such deposits. However, in common with all deposit types, if the principles that underpin the estimation, classification, and reporting procedures are borne in mind and common sense applied, most issues can be satisfactorily resolved. This paper discusses the classification and reporting of Mineral Resources for high-nugget effect gold vein deposits within the framework of the JORC Code (JORC, 1999). You do not currently have access to this article.

Uncertainty with regard to estimated grades and tonnages of a mineral deposit demands risk assessment in order to build investor confidence and attract the interest of other stakeholders in the success of a project. Uncertainties associated with Mineral Resource estimates can lead to unreliable production schedules and unpredictable cash flows. However, the techniques used in the mining industry to determine these uncertainties are inconsistent, because the important decisions taken in the process are solely dependent on the responsible Competent Person (CP), without limitations. This leads to disparities between different CPs' results, using data-sets from the same drill-holes. The various standard codes for public disclosure provide guidelines and recommendations for the classification of Mineral Resources and Reserves but do not provide details on, for example, the amount of geological and geostatistical information needed to qualify for each category of Resources and Reserves. The parameters used to generate the classification categories are subjectively assumed by the responsible CP. In this paper we investigate the impacts of different CPs' judgements on resource classification, using the same data-sets. The results underpin the need for the mining industry to develop a uniform Mineral Resources and Reserves classification framework that can minimize or avoid significant discrepancies that lead to potential misleading public disclosures.

The varied types of mineral deposits and geological features around the world have led to the creation of a large number of techniques, methodologies, and definitions for mineral resource classification. The most common methods used in the mineral industry include kriging variance, drilling spacing, neighborhood restriction, and conditional simulations. These methods generally do not use reconciled production information, only long-term borehole information based on personal judgment for defining confident intervals/limits on the mineral resource classification. A drilling spacing back analysis study for defining mineral resource classification was completed considering tonnages and grades confidence intervals related to its respective production volumes, based on short-term production reconciliation of analog deposits. The definition of adequate drill holes spacing and detailed results for classifying mineral resources are demonstrated by both an open-pit and an underground project adjacent to an existing mining operation. This study has considered a Brazilian sulfide deposit (Cu-Au) operating mine as analog information.

A pivotal aspect in the evaluation of mining projects is the classification of mineral resources, which directly influences the definition of mineral reserves and significantly impacts mine planning and operational stages. However, the current classification methodologies often need specificity regarding the methods and parameters employed and heavily rely on the qualified/competent person’s judgment. This study addresses these gaps by proposing a pioneering fuzzy approach to assess grade and tonnage uncertainties. By allowing for overlapping resource categories and directly incorporating economic, geological, metallurgical, environmental, and operational criteria, we aim to provide tools for decision-making and for the final classification and public disclosure of mineral resources and reserves. We demonstrate the potential of our proposed methodology through an application to an iron ore deposit case study and through a detailed discussion on its uses, contributions, strengths, weaknesses, and on whether it complies with international reporting codes.

The 3D geological models are the basis for delineating the mineralised domains which underline the whole process of the mineral resource estimation and also serves as a key parameter for selection of the mining methods and eventual conversion of the resources to reserves. Degree of the geological model’s uncertainty need to be estimated during the project evaluation studies and the risks of a project’s failure caused by incorrectly interpreted geology quantified and incorporated into classification of the mineral resources and ore reserves

The classification of mineral resources is crucial for public disclosure and is used to evaluate the risk associated with the mineral deposit, enabling informed decisions. To address this need, this study proposes the use of a multi-layer scorecard workflow (MLSW) for mineral resource classification that considers multiple factors from different disciplines. This approach is highly flexible as the competent user may adapt the scorecard workflow to the particularities of each deposit. In this paper, we considered classical metrics for resource classification, such as the number of samples, the slope of regression, kriging efficiency, and kriging variance, combinedwith more modern ones (Risk Index), which contemplates the combination of the estimation error, and geological continuity by a probabilistic approach. The methodology can also incorporate qualitative information such as the geological complexity. The proposed workflow has been applied in two different databases, demonstrating its transparency, auditability, and applicability.

Estimation of mineral resources and reserves with low values of error is essential in mineral exploration. The aim of this study is to estimate and model a vein type copper deposit using ordinary kriging method. After studying of variograms in different directions it was found out that the ore deposit has no anisotropy. The best fitted variogram model was considered for ordinary kriging estimation. Cross-validation was used to evaluate the accuracy of the variogram model for kriging. After trial and error a variogram with the best summary statistics was chosen. Model consists of a pure nugget effect with 0,30 amplitude plus a spherical scheme with sill 1.10 and range 30 m. The cross validation results showed that the correlation coefficient between estimated and real data was 0.829. The resource was classified based on calculated estimation errors by JORC code. Results showed that ordinary kriging can be used to model and estimate the vein type deposit. Consequently a three dimensional model of estimated value and error estimated value was provided by ordinary kriging to divide the ore into an economic and uneconomic part.

The role of mineral resource assessments in ecological stewardship

Mineral resource evaluation is one of a whole spectrum of quantitative methods which have been used since biblical times for the purpose of improving the process required to aid problem-solving and to increase the quality of strategic management decisions at all levels. Such evaluation leads to the classification of mineral resources. It is important to have a clear knowledge of how mineral resources are classified and what the classification nomenclature actually means to ensure decisions are based on a sound understanding of criteria applied. As interest for investment in mineral resource development spreads into Eastern Europe and the former Soviet Union, the need for some form of harmonization in terminology, nomenclature, criteria, as well as the approach to the classification process for the definition of mineral resources has again been highlighted. The approach to quantitative studies is discussed, and guidelines are given that may improve mineral reserve and resource estimates so that implementation failure of mining projects can be avoided. Approaches to the classification of mineral resources are reviewed along with the uses made of such mineral resource information. The argument is put for a standard approach to mineral resource classification and consistency of international nomenclature with a discussion of some of the related problems. Principles are established which may suggest a way forward illustrated by a recent major resource assessment project.

Mineral resources are estimated to establish potential orebody with acceptable quality (grade) and quantity (tonnage) to validate investment. Estimating mineral resources is associated with uncertainty from sampling, geological heterogeneity, shortage of knowledge and application of mathematical models at sampled and unsampled locations. The uncertainty causes overestimation or underestimation of mineral deposit quality and/or quantity, affecting the anticipated value of a mining project. Therefore, uncertainty is assessed to avoid any likely risks, establish areas more prone to uncertainty and allocate resources to scale down potential consequences. Kriging, probabilistic, geostatistical simulation and machine learning methods are used to estimate mineral resources and assess uncertainty, and their applicability depends on deposit characteristics, amount of data available and expertise of technical personnel. These methods are scattered in the literature making them challenging to access when needed for uncertainty quantification. Therefore, this review aims to compile information about uncertainties in mineral resource estimation scatted in the literature and develop a knowledge base of methodologies for uncertainty quantification. In addition, mineral resource estimation comprises different interdependent steps, in and through which uncertainty accumulates and propagates toward the final estimate. Hence, this review demonstrates stepwise uncertainty propagation and assessment through various phases of the estimation process. This can broaden knowledge about mineral resource estimation and uncertainty assessment in each step and increase the accuracy of mineral resource estimates and mining project viability.

Mineral resource classification plays a critical role in communicating confidence levels, yet supporting methodologies such as drill-hole spacing analysis and geostatistical simulations are not consistently applied in routine updates of deterministic resource models. As a result, both local and global uncertainty quantification remain underutilized, and drilling requirements are often defined without a clear link to uncertainty reduction. This paper introduces a mineral resource uncertainty and drilling policy framework developed and applied at Compañía Minera Doña Inés de Collahuasi (CMDIC). The framework quantifies the uncertainty of each mineral resource model update when new data are available and provides an initial approach to determining drilling requirements based on CMDIC’s risk acceptance policies for different project stages. The proposed approach is a stochastic workflow that uses the current deterministic mineral resource model and database to generate geostatistical simulations. These simulations account for data quality, quantity, geological variability, and copper-grade variability. They form the basis for mineral resource classification with an explicit uncertainty quantification and provide an optimized drilling campaign to achieve desired risk levels subject to budget constraints. Because stochastic modeling updates faster than deterministic modeling, it provides timely insights from new drilling campaigns and delivers valuable insights for subsequent deterministic geological and grade modeling updates. The implementation of this workflow demonstrates its feasibility as a standard step following deterministic modeling, leading to cost-effective mineral resource development and management by aligning technical practices with the organization’s strategic objectives and risk preferences.

Estimation of mineral resources and reserves with low values of error is essential in mineral exploration. The aim of this study is to compare inverse distance weighted (IDW) and ordinary kriging (OK) methods based on error estimation in the Dardevey iron ore deposit, NE Iran. Anisotropic ellipsoid and variograms were calculated and generated for estimation of Fe distribution by both methods. Density, continuity of ore and waste, the number of points involved, and the discretization factor in the estimation of ore and waste boundaries were determined and the resource estimated by IDW and OK methods. Estimation errors were classified based on JORC standard, and both methods were compared due to distribution of error estimation. Results obtained by the study indicate that error estimation of OK method is less than IDW method and that the results of OK method are reliable.

There are two main issues stemming from Vallee’s (2002) comments on Dominy et al. (2001b). First, he raises the importance of quality assurance/quality control (QA/QC) during resource estimation programs, and second, he indicates that the resolution and understanding of continuity (grade and geological) issues are paramount in the classification of resources. In particular, continuity is critical at the boundary between the Inferred and Indicated Mineral Resource categories. The original contribution agrees with the importance of these points, and the principal author offers the following discussion and clarification. Further comment is also provided on the reporting of grade uncertainty through grade ranges. It is worth remembering that vein-gold deposits (e.g., shear-zone, lode-gold, mesothermal types etc.), particularly those containing coarse gold particles (e.g., >100 μm), generally show large variations in grade and an erratic grade distribution. In most cases, diamond drilling is a good measure of geological continuity (depending on drill spacing), but a poor measure of grade and its distribution. High-nugget effect deposits rank among the most difficult deposit types in terms of producing an accurate and precise resource estimate, and clearly represent a distinct deposit type. ### Quality Assurance/Quality Control The issue of quality assurance (QA) and quality control (QC) during exploration, estimation/evaluation and exploitation has been at the forefront of industry perceptions since the Bre-X affair in the late 1990s. The need for QA/QC programs is paramount in any mining/minerals project (Vallee, 1998a, 1998b, 2002). Quality is a critical issue at all stages of a minerals project. For example, QA/QC of analytical and test data, and the corroboration of geological data within the resource/reserve estimation process must now be considered mandatory. Indeed, the Canadian National Instrument 43–101 requires mandatory QA/QC programs (CSA, 2001). The 1999 JORC Code does not prescribe QA/QC programs but, for example, QA/QC for sampling/assaying is …

In terms of the reporting codes Mineral Resource classification is a function of increasing confidence in the geoscientific information and the associated resource estimate. An overview of Mineral Resource classification approaches is given; the tendency in resource classification is to concentrate on the confidence associated with the grade estimate. Uncertainties linked to tonnage and metal estimates are rarely explicitly mentioned. As for the risk associated with the underlying geological model it is often, if at all, only considered on a global rather than a local basis. The objective is to present a quantifiable Mineral Resource classification guideline that recognises uncertainty in both geological and resource models, considers confidence in estimation of metal content for specified production periods and also takes into account both the correlation of blocks in the block model as well the change of support between an estimated block and the production period. This classification method builds on a previous publication (Dohm, 2003), where a technique for assessing the combined risk associated with both the geological and grade models was demonstrated. The final result is a succinctly classified mineral resource model, which is based on objective quantifiable classification rules that recognises the uncertainty related to subjective interpretations of the available information.

Research Article| January 01, 2002 Comments on “Classification and Reporting of Mineral Resources for High-Nugget Effect Gold Vein Deposits,” by S.C. Dominy, P.R. Stephenson and A.E. Annels MARCEL VALLÉE MARCEL VALLÉE Géoconseil Marcel Vallée inc., Sainte-Foy, Québec, Canada G1X 3J9 Search for other works by this author on: GSW Google Scholar Exploration and Mining Geology (2002) 11 (1-4): 113–117. https://doi.org/10.2113/11.1-4.113 Article history first online: 02 Mar 2017 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Search Site Citation MARCEL VALLÉE; Comments on “Classification and Reporting of Mineral Resources for High-Nugget Effect Gold Vein Deposits,” by S.C. Dominy, P.R. Stephenson and A.E. Annels. Exploration and Mining Geology 2002;; 11 (1-4): 113–117. doi: https://doi.org/10.2113/11.1-4.113 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyExploration and Mining Geology Search Advanced Search This text consists of two parts, comments on “Classification and Reporting of Mineral Resources for High-Nugget Effect Gold Vein Deposits,” by S.C. Dominy, P.R. Stephenson and A.E. Annels, Volume 10, No. 3, pp. 215-233, and related comments on some features of the JORC Code. This paper is, in many respects, a very elaborate and valuable account of the problems encountered in the estimation and classification of high-nugget gold deposits. However, in some key areas, it appears to fall short of its objective to help “resolve satisfactorily most issues” and “contribute to improved estimation and classification” (abstract). One must realize that... You do not have access to this content, please speak to your institutional administrator if you feel you should have access.