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By P Kittler

In the spectrum of mining-related disciplines, geometallurgy sits between resource geology and metallurgical engineering. Unlike its neighbours, it is a relatively young discipline and its concepts, procedures and techniques are still evolving. Relevant geometallurgical issues differ from one deposit to the next so there is no single template that can be used in all situations, meaning that geometallurgy programs are nearly always customised and tailored to the deposit and the situation.Over time, a number of key mistakes are seen to emerge regularly. The eventual impact of these mistakes can vary from suboptimal performance of mine and plant to complete failure of the operation. For instance, poor characterisation of ore lithologies and their spatial distribution can have negative impacts on processing plant throughput. However, when combined with failure to identify critical blending constraints it can result in a totally inoperable plant.A number of common mistakes committed during geometallurgical programs are discussed and some preferred alternative approaches are suggested

Sep 5, 2011

By J E. Everett

An essential requirement of product quality control in the mining industry is to be able to reliably predict key quality properties of finished product from the data available before the extraction of the ore. From a production viewpoint, the unit of data collection is generally the input and output data set for each shift of crusher production, but could be any period where mine precrusher data can be reliably matched with product data.This paper demonstrates that linear regression models can be used to predict crush grades from blast grades, even where the crush material is blended from multiple sources or pits for each of which differing regression models might apply. The advantage of using increasingly complex regressions models to match the available data to give increasingly more powerful models is examined. The best model for any application will be a balance between required predictability, available data and the business’ tolerance for complex models.The regression modelling approach has several advantages over the more classic method of run-of-mine (ROM) crusher trials, which will be discussed.The models can use any predictor variable such as grade, geotype and in situ density provided the preextraction data set can be reliably matched with post-crusher data and is significant as a predictor. The models have been used extensively in the generation of the daily crusher plan with the aim of maintaining finished product grade. This approach has also been used associated with exploration drilling and long-term planning.It is acknowledged that there are inherent problems in fitting lump and fines grade to a linear model. However, these problems are minor when they are used for interpolation within the window spanned by the shift blend records used to produce the model. This paper discusses some of the issues limiting linear regression models in this application, and suggests methods enabling consistent models to be formulated

Sep 5, 2011

By S Walters

The emerging discipline of ‘geometallurgy’ is not new but is becoming increasingly recognised as a discrete and high-value activity. Geometallurgy involves a quantified and spatially constrained approach to ore characterisation in terms of relationships to critical processing performance behaviours. A key outcome is enhanced economic optimisation of mining and improved risk management tools.Industry recognition of the need for large-scale, integrated research to support this emerging area resulted in commencement of a major ongoing initiative in mid-2005. The AMIRA International P843 ‘GeMIII’ project (Geometallurgical Mapping and Mine Modelling) is a cross-discipline collaboration involving a number of world-class research organisations supported by over 20 industry sponsors.The project is delivering integrated tools and methods which link intrinsic rock characteristics to prediction of mineral processing performance. These indices must be capable of spatial distribution in 3D resource models and incorporation into mine planning and optimisation. A key aspect is provision of new cost-effective tools and methods which can provide the large numbers of measured and estimated data points required to undertake effective modelling and spatial distribution. The integrated methodology and enabling technologies being developed through the ongoing AMIRA GeMIII project are outlined, together with implications for supporting industry best practice.

Sep 5, 2011

By J Jackson

The term geometallurgy (or geometallurgical) first appeared in the literature some 40 years ago and has a wide range of definitions and perspectives which increases the barriers of communication between practitioners and across functional groups and management levels within organisations. However the consistent theme running through the diversity of definitions and perspectives is the requirement for close collaboration between the relevant disciplines. This concept of geometallurgy arguably goes back to the earliest days of the exploitation of mineral deposits, through the mid-1500s to the beginning of the 20th century.This holistic and integrated view was diminished during the 20th century due to a number of factors which included the commencement and continuing upward trend of large-scale mining and the expansion in production, the rise of scientific and classical management theories and methodologies, increase in specialisation and the development of traditional project planning. The re-emergence of geometallurgy in the late 20th century required the relevant functional units and disciplines to collaborate as a team. This necessitates a common understanding that the geometallurgical program’s goals must be obtained, which requires clear and consistent communication, particularly with the modern trend of virtual teams.A multi-dimensional construct is proposed to assist in providing a consistent approach in the communication of the context and purpose of both general and specific geometallurgical programs, thus simplifying communication. This geometallurgical construct consists of the dimensions of process span, response knowledge, variability knowledge, spatial knowledge, application and project phase in relation to extraction process(es) applied to geological materials from a mineral deposit. These dimensions are then linked to the phase of mineral project development in which the geometallurgical program is being applied, which ranges from advanced exploration through to operational execution. This paper outlines the multi-dimensional construct with examples of how the context, purpose and scope of geometallurgical programs can be clearly delineated and communicated.

Sep 5, 2011

By R Berry

This paper looks at two ways to estimate the bulk mineralogy of the rocks for assay intervals. The aim is to find an efficient indicator of the most common minerals in the rock. Phase (modal) analysis has traditionally been done using visual methods such as point counting and image analysis. A modern version of this process is the X-ray point counting routine using the SEM-EDS based software. These methods are too slow and expensive for routine analysis of bulk sample mineralogy at the normal assay spacing.Two sources of data were considered that provide information that can be used to determine the mineral abundance in assay samples. The most widely applied method is (semi-) quantitative X-ray diffraction (QXRD). The QXRD method is most applicable to major minerals and has limited application to minerals at low abundance. The nominal detection limit is 0.5 per cent. Values below five per cent have large errors. A second, less common, method is calculation of mineralogy from chemical assay data. Conversion of chemical analyses to mineralogical analyses depends on the unique chemical composition of each mineral. Elements only found in one mineral are easily accounted for, but many compositions are ambiguous. Deciding on the actual mineralogy is not simple. Recalculation of mineral mode from chemical analyses is more accurate than QXRD when the correct minerals, and mineral compositions, are known.Where only a few QXRD analyses are available they can be used to setup a protocol for calculation of mineralogy from assay data. Linear programming works well in this environment. The best results are obtained when both H2O and CO2 are directly measured. Loss-on-ignition (LOI) should be included if these are not available.Where both QXRD and chemical analysis are available for all samples, the best results are obtained using the least squared method to merge the data sets assuming QXRD has much higher analytical errors than chemical assays. The combined method provides more robust results because the high abundance minerals are controlled by the QXRD measurements while the chemical assays improve the precision for low abundance minerals

Sep 5, 2011

By S C. Dominy

Gravity amenable gold ores are those that after comminution produce liberated particles, composites and/or carriers that can be recovered by gravity separation means. Recovery depends upon mineralisation type and the comminution and concentration method used. Any in situ ore with more than 20 per cent of gold greater than 100 µm in size is likely to have a strong gravity recoverable component. A less traditional approach is where the gold is sulfide-locked and the gold carrier plus any liberated gold is recovered via continuous gravity recovery following fine crushing. Specific tests are undertaken to determine the level of gravity amenable gold within a deposit. The principal methodology is the gravity recoverable gold (GRG) test, which consists of three sequential liberation and recovery stages. For continuous gravity recovery (CGR), the sample is fed over a laboratory Wilfley table to simulate continuous recovery of the gold. Tails from the feed can be progressively re-ground and re-tabled to develop a grade-recovery curve. Primary geology and gold particle size-distribution have a strong effect on the mass of sample required to provide representative results for both tests. Where low-grade ore contains very coarse gold, substantially larger sample masses are required. Experience has shown that GRG and CGR values can vary throughout an orebody, particularly within different geological-grade domains. A testing regime must consider sample frequency and support.

Sep 5, 2011

By S Dunham

A cross-discipline approach combining geology, metallurgy and mine planning, geometallurgy is seen as the next logical advance in improving the design and operation of mining businesses. This rapidly evolving field is focused on realising the value that has often been lost in the chasms between the aforementioned technical silos. When successful, the geometallurgical approach promises the ability to tailor mining and mineral processing development and operational options more closely to the characteristics of the resource with resulting lower costs (both capital and operating), improved recoveries and fewer project failures.As with any technological (or managerial) change, there will be side effects from the success of the geometallurgical approach that will impact areas beyond obvious linkages between geology and processing. Some of these effects have the potential to be beneficial to the resource industry and society in general. Beyond the basic geometallurgical promise of closer alignment between processing design/strategy and the multivariate 3D spatial characteristics of the mineralisation, this paper discusses the broader implications and ramifications of the emergent geometallurgical approach: enhancing exploration and discovery; improving project development success; achieving sustainability in an industry working with non-renewable resources; in a world of increasingly hard won license to operate, improving the management of potentially harmful by-products and waste materials (with knock on effects for proactive management of closure issues); smarter approaches to energy management and thus optimisation of projects with respect to carbon footprint; and potential to re-value assets and thus realise a strategic advantage in the area of acquisitions, divestments, mergers and take overs. The authors strongly believe that companies that actively engage with the above issues (and ensure that technical efforts in the area of geometallurgy are not isolated from the larger value chain activities of the firm) will be able to exploit the opportunities that consequentially arise. These early adopters will gain first mover advantages. Firms taking the broad view of geometallurgy will have an opportunity to maximise stakeholder value and win greater success than their less insightful competitors.

Sep 5, 2011

By L Keeney

Cu-Au porphyry deposits are large tonnage low-grade resources dependent on processing large volumes of material. The ability to understand and predict comminution performance is critical, as it is frequently the rate-limiting step during processing. Parameters required from geometallurgical mapping and modelling of comminution performance are: A×b processing domains based on impact breakage variability, and BMWi processing domains showing grind response variability which lead to Mine throughput domains for a specific comminution circuit configuration. Due to the cost and/or large sample requirements of traditional comminution tests, insufficient data density and distribution of comminution indices commonly exist, making it difficult to model and map deposit scale variability. One method routinely applied to three dimensional domaining is to have an a priori zoning based on geological features (lithology, alteration, mineralogy, textures), and then apply to all material within a given domain the metallurgical values measured on a single sample or average of samples from that domain. The problem with this approach is that commonly the geological domains do not reflect comminution response, therefore compromising the quality of the average domain estimate. To resolve this problem an alternate integrated geometallurgical mapping and modelling method is applied to model and map comminution performance at Cadia East. This paper uses a data set of ~32 000 assay measurements and geological logging information, integrated with ~150 comminution measurements to analyse and develop proxy support models for comminution indices (ie A×b, BMWi, and throughput) based on inherent geological variability. Discrete downhole comminution processing domains are created, mapping orebody response while maintaining inherent variability.

Sep 5, 2011

By S Leichliter

La Colosa, Colombia is a large gold-porphyry deposit currently undergoing feasibility studies by AngloGold Ashanti. This period of development allows the opportunity to apply innovative and emerging testing and modelling methods to provide a predictive geometallurgical recovery model for the deposit. By partnering with the AMIRA P843A Geometallurgical Mapping and Mine Modelling (GeMIII) research project, the geological and mineralogical data are analysed with respect to liberation and recovery methods. This will allow the variability in the geology and mineralogy of the deposit and key relationships recognised in the data to be included in the development of a model to help predict recoveries of the gold. Many aspects of the gold mineralisation such as gold paragenesis, associations, grain size and texture are determined and analysed using optical microscopy, Mineral Liberation Analysis (MLA), and laser ablation (LA-ICP-MS). At La Colosa the gold has been located in sulfides (eg pyrite, pyrrhotite, and chalcopyrite), silicates, along silicate phase boundaries, and among the pyrite-rich intermediate argillic alteration. Further testing and analysis will also determine if ‘invisible’ gold is present in the pyrite, pyrrhotite, and chalcopyrite. Recovery methods can also be determined and tested after combining the results of mineralogy tests with other data (eg results of comminution tests). Indices for the mineralisation and recovery are developed using image analysis and analytical testing. These indices are correlated with recovery data from the company to develop a predictive block model for recovery. The goal is to develop cost-effective, efficient methods to analyse the mineralogy and geology of the deposit; understand the variability in the mineralogy, geology, and recovery; and construct a predictive geometallurgical recovery model.

Sep 5, 2011

By J E. Everett

A block model of the orebody is used to plan a mine. The first requirement is to identify ore to be extracted, discarding waste, so as to meet target grade, generally in multiple analytes. Commonly, cut-off values are set for each analyte, so as to distinguish ore from waste. It will be shown that, if more than one analyte is important, this procedure is wasteful of ore and a composite cut-off function is preferable.A second requirement is to sequence the ore extraction so that the variability in ore grade is controlled: failure to do so will result either in low-quality ore being marketed, or excessive re-handling being required to blend the ore to reduce the grade variability.A third requirement is that the extraction sequence should be such as to control the amount of equipment movement required, enhancing the equipment productivity.The overall objective, to optimise the Net Present Value (NPV) of the mined ore, is simply stated but complex in realisation, since so many factors, such as target grade, grade variability, equipment choice, equipment movement, and downstream blending all have alternatives which can be traded off against each other, and all of which contribute to the costs and benefits making up the total NPV.There exist commercial packages for ore selection and mine planning. However, they are often of a ‘black box’ nature, and users are sometimes not fully aware of the criteria being applied. In particular, the treatment of quality as a multidimensional vector may be problematic. The purpose of this paper is to discuss some of the issues involved and to suggest an alternative set of approaches, which should complement existing commercial treatments.The discussion will be illustrated specifically by reference to the mining of iron ore, but the issues are relevant to a wide variety of mining situations

Sep 5, 2011

By B Newcombe

From many years practical industrial experience the author has drawn together a list of the key points to consider when embarking on a large-scale geometallurgical project. Fundamental aspects which must be considered include (but are not limited to):understanding the variability of the orebody both laterally and with depth;comminution properties and variability;flotation properties, requirements and variability – differing ore zones may have substantially different requirements for optimal performance;the role of texture – the consequences of both grain size and distribution and how the circuit will handle these;realistic expectation in terms of the financial requirements and time frame results are required within; andintegration of, and communication between key personnel within the various departments of the site (mining, geology and processing) is a fundamental aspect of any geometallurgically based processing system. An example of a highly complex nickel sulfide ore will be used as the basis of discussion.

Sep 5, 2011

By G Harbort

The use of geological data for optimisation of operating plants has become a significant part of the modern process mindset. The underlying principle is to convert spatial geological data into metallurgical information to drive production and mine planning, blast design, blending strategies and plant set-up. Increasingly the process designer is required to interface with geological and mining staff to propose the best investment strategy for a project’s benefit.The geometallurgical model development for the Zafranal copper-gold project commenced in March, 2010, several months after the start of a major drilling campaign by AQM Copper Inc. The initial evaluation stage required review of historical Teck RC data to determine areas of metallurgical interest within the project. As drilling transitioned from RC to diamond drilling, core that was considered representative of the resource was selected and subjected to MLA mineralogy analysis. When coupled with sequential copper analyses this allowed calibration of an assay derived mineralogy model, allowing process concept options and a preliminary test program to be developed. This Phase 1 test program provided a preliminary process design, operating costs and anticipated concentrate grades and recoveries. Both this data and resource estimation were used to generate a preliminary Whittle pit optimisation based on maximising net smelter return, which in turn fed into a preliminary mine plan. The preliminary mine plan, in conjunction with geological logs and core results was used to develop a phase 2 metallurgical test program, where variability tests were conducted throughout the resource and composites formed to investigate specific years of the mine plan in locked cycle flotation. In addition comminution tests were modelled against geometallurgical domains allowing comminution components values to be calculated for 36?000 m of core. A flotation geometallurgical model was developed and incorporated into the resource model allowing concentrate grades and recoveries to be estimated on a block-by-block basis.

Sep 5, 2011

By J N. Farrell

Metallurgists frequently use a small number of large samples to characterise expected mill performance, with the a priori assumption that these few samples are representative of the entire deposit. Geometallurgical sampling is becoming more prevalent as a way to use many smaller samples to indicate the expected recovery as part of the Mineral Resource or Ore Reserve estimation.The evaluation of magnetite deposits involves specialised approaches to sample preparation, sample analysis and resource estimation. The sampling protocol for magnetite deposits will typically include the use of Davis Tube analysis. The Davis Tube is a mini-pilot plant that is used for determining the mass magnetic recovery from a small amount of sample ground to a specific size. The magnetic concentrate may then be analysed and together with the mass magnetic recovery, may be used to simulate the expected mill recovery. The magnetite sampling protocol is specific to each deposit, and is typically designed around a specific metallurgical flow sheet.During resource modelling, the mass magnetic recoveries and concentrate grades may be estimated directly or assigned to a block model using regressions with other chemical constituents. For estimation, the concentrate grades are weighted to account for the different mass magnetic recoveries of each sample and the impact of these differences on the concentrate estimates.

Sep 5, 2011

By L L. Kuhar

The AMIRA International P843A Geometallurgical Mapping and Mine Modelling (GeMIII) project is an interdisciplinary research collaboration between 17 global mineral companies and four research organisations aimed at providing support for the emerging industry trend of geometallurgy. The main aim of the project is to develop predictive geometallurgical indices which can be incorporated into a resource block model to maximise the commercial value of an orebody and to minimise the social and environmental impact of mining operations.Researchers at the Parker Centre/CSIRO have been focusing on the development of predictive hydrometallurgical indices for the GeMIII project via rapid, small-scale, cost-effective tests and protocols for comparative ranking of attributes relevant to leach performance. The focus of the small-scale leach tests has been to determine the leach index (relative indication of leach performance), recovery, impurity deportment, reagent consumption, effect of size/degree of liberation and mineralogy of samples for their relative ranking. Predictive tests for heap leach hydrology (related to agglomerate characteristics) and slurry rheological behaviour are also being developed.

Sep 5, 2011

By C Lower

An unprecedented, quantitative, deposit-scale understanding of major clay mineral distribution throughout the Yeelirrie uranium deposit has been achieved by adapting the existing HyLogger™/HyChips™ analytical technique to measure resource assay pulps. Spectral analysis via HyLogger™ and HyChips™ systems has previously been used as a small-scale geometallurgical research tool, but never before has it been quantitatively applied to an entire resource sample population for the purpose of developing stand alone block models for major clay minerals. Economic uranium mineralisation within the thin but extensive Yeelirrie?palaeochannel?primarily exists within the transition zone between a groundwater-derived calcrete and the underlying clay-quartz regolith. Early geometallurgical investigations identified that certain clay minerals could be deleterious to the efficiency of the uranium recovery process. Accurate calculation of clay mineralogy from resource assays proved too difficult due to chemical similarities between co-existing kaolinite, smectite and magnesium carbonate minerals. A relatively low-cost, efficient and accurate method of quantitative clay mineral analysis was required to measure over 70 000 resource assay pulps. After initial testing and comparison with QXRD, the HyChips™ 3-1 spectral logging system was implemented as a primary method of clay mineral analysis for the Yeelirrie uranium deposit. The resultant spatial data set allowed the interpretation and geostatistical analysis of geometallurgical domains across the deposit. Clay block models were generated and used in geometallurgical resource calculations to benefit the mine planning process.

Sep 5, 2011

By J Vann

Geometallurgy is inherently multidisciplinary and it gains its power to deliver improved outcomes for the minerals industry by bridging silos between disciplines. This cross-disciplinary approach unlocks value, identifies (and avoids) value destruction and provides a pathway to better implementation of an energy efficient and sustainable minerals industry. Despite this promise, we argue that the cross disciplinary approach also represents the greatest threat to the success of geometallurgy. Some key cross disciplinary issues identified are:Geometallurgy has so far evolved in the technical sphere, mainly by collaborations of geologists and metallurgists. Consequently, in many cases the business dimensions and value have not been incorporated. Financial and risk evaluation viewpoints need to be assimilated into the geometallurgical paradigm. In fact, until such measures are integrated into practices, the value cannot be fully demonstrated.The historical and current efforts on geometallurgy have been highly focused on improved sampling and measurement of geometallurgical properties or appropriate proxies. This work has been driven by professional and academic geologists and metallurgical engineers. It is notable that mining engineers (who are critical in the value delivery of geometallurgy through changed planning approaches) have so far been at the margins, or absent. The construction of valid three dimensional (3D) geometallurgical models raises some important organisational, mathematical and geostatistical considerations. The enhancement of resource models to account for geometallurgical aspects must be managed carefully if the full value of geometallurgical thinking is to be realised.The use of 3D geometallurgical models to change business decision making and practices is the real nub of the problem. We believe sophisticated scenario based evaluation methods are required if the potential impacts of spatial rock properties on processes further down the mining value chain are to be realistically evaluated. Such changed practices require ‘whole of value chain thinking’ to embed this change into the business in an ongoing way.Because profitable geometallurgy demands changed approaches to business decision-making and also organisation of the firm (radically changed in some cases), the senior executive viewpoint must be incorporated into the language and thinking of geometallurgy. This is unavoidable if the establishment of a geometallurgical approach to project evaluation, development and operation is to be successful and sustained.A powerful analogy for the problem faced by geometallurgy is the transformation of safety culture in the mining industry in the past 20 years. We argue that, as in the safety revolution, explicit models, cultural change and management of the ‘soft’ dimensions of the problem are pivotal to success of geometallurgy. In light of this, we propose a model (‘the Geomet Curve’) that gives a framework and pathway for geometallurgy in seven steps. The implications of this model for technical and organisational steps required at different stages are discussed.

Sep 5, 2011

By E Macmillan

For any processing plant there will be deleterious elements or minerals which affect production or quality, and a strategy to predict their occurrence and mitigate their impact is required. An early objective of the Olympic Dam geometallurgy program was prediction of the acid consumption and gelling behaviour of the ore, which affects leach recovery, throughput and circuit availability. The geometallurgical test program measured the response of a wide range of ore types, and then relationships between mineralogy, metallurgy and chemistry were developed. Thus the drivers of acid consumption and triggers for gelling events could be described in ways that were meaningful to plant operations.To enable optimisation of the mine planning and ore blending processes, the parameters driving metallurgical response must be translated to the block model. Data support is critical in populating a block model. Assay information is easily obtained, but mineralogical and metallurgical information is more expensive and time consuming to collect. Hence population of a block model with mineralogical and metallurgical values using a conventional geostatistical approach is not always possible.An alternative approach employed at Olympic Dam was the development of a suite of assay-to-mineral algorithms. These algorithms were calibrated using all available mineralogical information, and this enabled the calculation of mineral abundances for more than 1.5 million assayed samples. Estimation of the minerals in the block model, and the generation of acid consumption values for each block could subsequently occur.The use of back-calculated mineralogy from assays as part of process model generation is an innovative tool which can be used for mine planning and optimisation and to ensure the plant does not receive any unwelcome surprises. Ultimately, one purpose of geometallurgy is to predict potential processing issues and their drivers so that these can be mitigated. A strategy to achieve this is outlined hereafter.

Sep 5, 2011

By M J. Newton

As part of the AMIRA P843 project Keeney and Walters (2008) describe how the principal components analysis (PCA) method can be used to identify classes of samples with similar metallurgical characteristics. However PCA does not take account of the location of each sample in 3D space. This paper describes a study to analyse the spatial distributions of the classes and determine appropriate modelling methods to control the estimation of the comminution parameters and mill performance indices into a 3D block model. This includes but is not restricted to geostatistical techniques that are used for grade modelling. The starting point for the study is the class definitions and the regression models for estimating the comminution parameters and performance indices. Each sample is assigned a class and the spatial distribution of each class has been analysed using different techniques including the calculation of variograms. This establishes whether the classes can be used as domain control for estimation into the block model – a procedure similar to using geological rock type domains for controlling the estimation of grades for a resource model. However, standard geostatistical estimation methods such as ordinary kriging assume that the variable being estimated is additive or linear. This assumption is valid for variables such as grade but this is not necessarily the case for the comminution parameters or the performance indices. Therefore the estimation procedure must either use variables that are definitely additive or the effect of potential non-additivity must be tested and accounted for where possible.The results of the study show how performance indices such as recovery, throughput and specific power can be estimated into the block model. This model is then combined with the geological model to provide a multi-parametric model that is used to optimise the mine planning and scheduling activities which can lead to significant improvements in NPV.

Sep 5, 2011

By R C. A Minnitt

The Witwatersrand Supergroup is host to the world’s largest gold deposits. The location, quantity, grade, continuity and other geological characteristics of a Mineral Resource are either known, estimated from specific geological evidence and knowledge, or interpreted from a well constrained and portrayed geological model. Establishing such a model is an integral part of the evaluation process in the Anglogold Ashanti (AGA) group, and is referred to as a Geoscience Business Model for the mining operations. The geological model established over the years has relied on detailed descriptions of the mineralisation, and understanding of the regional and local geology of the Witwatersrand sediments, in the West Wits and Free State goldfields. The processes of data collection in regard to underground stope and developing mapping, structural and sedimentological mapping, and the geological inputs to mine planning systems are described, together with aspects of drilling, sampling, grade control, and data management. Anglogold Ashanti’s Geoscience Department employs a complex and unique process to generate the Mineral Resource, including a 3D geological structure model of the orebodies which are usually highly faulted, and a robust mineralisation/facies model on which the decisions about domain boundaries are established. A Mineral Resource Inventory System (MRIS) has a dynamic graphic interface to the resource database that is used for compiling the resource statement. It is also used to determine the gold-called-for, as well as short and long-term planning. A unique estimation process using the extensive closely spaced stope face sampling database and the more widely spaced surface drilling information is used in combination in the Macro Co-Kriging estimation. The use of different support sizes in the C0-Kriging process is preceded by the equally unique ‘clusterisation’ and ‘acceptorisation’ procedures by which the data are quality controlled and assured prior to their use in the kriging process. The Mineral Resource and Ore Reserve estimates are classified according to the SAMREC code through a method that ensures that they are compliant, auditable, transparent and reconcilable with previous Mineral Resource and Ore Reserve statements.

Aug 22, 2011

By M J. Nugus

The structural models developed by exploration and mining geologists are most commonly concerned with the ‘main structures’ or key constituents that pertain to the associated mineralisation. To this end, it is recognised that the geological and exploration models, which are developed, are essentially ‘fit for task’ and the scale-related variances are not always realised at an early stage. In fact, in many cases anomalous intercepts along different trends and of variable apparent continuity have often been neglected as ‘back scatter’ and not important. Recognition of the importance of subordinate structures as mineralising hosts suggests that additional opportunities exist outside the primary and main structures. History shows that opportunities are often not realised where gold is hosted in orientations not parallel to the predominant trend of the schistosity, main fault or shear zone.Another reason for the importance of subordinate structures can be understood from their roles in a percolation network. Subordinate structures can access the large fluid fluxes provided by backbone elements, but, as dangling elements, they provide greater opportunities for gold precipitation mechanisms and hence may constitute essential parts of the resource. Therefore, the role, understanding and consideration of the smaller-scale and orthogonal structures should be considered during all facets of exploration, resource development, delineation and mining.

Aug 22, 2011