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By L E. Howard

Micro-computed tomography (Micro-CT) is an X-ray imaging technique that produces high-resolution 3D computerised ‘virtual’ models of geological specimens based on the X-ray attenuation of different materials in the specimen. The ‘virtual’ specimens are made up of voxels, the 3D equivalent of pixels, each assigned a gray value based on X-ray absorption. Micro-CT has enormous potential for allowing non-destructive, digital analysis of structure and composition when the mineralogical phases can be digitally separated. However, variation in X-ray flux between scans results in highly variable grayscale values for identical materials. Consequently, materials usually have to be segmented manually, which is subjective, inaccurate, labour-intensive and time consuming. Here we present a protocol and imaging software package that can be used to accurately and precisely extract quantifiable information in a repeatable and objective fashion. The software will allow the separation, visualisation, measurement and analysis of spatial and volumetric distribution of different mineral phases without damaging the specimen. This paper explains the potential use of this protocol and software package to ultimately allow automatic segmentation and quantification of gold in drill core samples, removing subjectivity and inaccuracies from data processing.

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 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 V Liebezeit

Data management is a significant, but perhaps unrecognised, consideration for any organisation embarking on a geometallurgy program. While resource geologists are accustomed to dealing with thousands or millions of samples and their associated assays, metallurgists and mineralogists have traditionally carried out only a few expensive and complicated tests and consequently tend to manage the resultant data in spreadsheets. The emerging discipline of geometallurgy arrives with significant challenges as a result of the sheer volume of data generated. As an example, the Olympic Dam geometallurgy program has generated more than:60 000 physical subsamples analysed for up to 30 analytes; 25 000 spreadsheet-based laboratory test results; 30 000 sized fractions analysed via QXRD, MLA and/or QEMSCAN;multiple external providers of data, and more than seven terabytes of data. Considerations for any geometallurgy data management system include storage space, data integrity, accessibility, traceability (sample provenance), maintenance and ease of use, integration with data providers and the ability to modify the system to adapt to changing requirements. Further, aspects of a geometallurgy program, such as conducting a range of tests on one sample, producing subsamples such as concentrates and tailings, compositing and routine QA/QC may not be well handled by existing software and providers geared to standard geochemistry, mineralogy or metallurgical test programs. The development of a geometallurgy database using existing software (AcQuire), and the associated file management system will be described for a large geometallurgy program at Olympic Dam. As with many aspects of geometallurgy there is no approach that meets the needs of every site or program. Recognition of data management as an issue, a structured approach in the program planning phase and commitment to executing the strategy will increase the chances of successfully mining the geometallurgical data.

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 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 R Knight

The spatial variability of geometallurgical characteristics in a mineral deposit can have significant impact on financial and mine planning, mineral processing, concentrate quality and tailings composition throughout the life of a project. These characteristics that may determine the success of a project are not readily apparent to geologists unless they are integrated with deposit geology and assay data, modelled in 3D and incorporated into resource block models.One of the primary objectives of geometallurgy is to integrate and incorporate various geologic, geotechnical and geometallurgical parameters into resource block models, which typically are limited to in situ tonnes and grade of economic metals. Critical attributes, such as concentration of deleterious elements that affect the project viability need to be identified and integrated into the resource block model early in the exploration stage to predict project profitability and address potential environmental impacts.Using data from a volcanogenic massive sulfide deposit we present an example of the integration of ore geochemistry (cadmium, arsenic and antimony) with the results of preliminary, bench-scale metallurgical test work in a 3D geometallurgical model that predicts subdomains of poor-quality concentrate and tailings containing environmentally-sensitive elements. This critical information was incorporated into the existing resource block model in order to inform both financial modelling and mine planning.This example demonstrates that geometallurgical data can be effectively integrated with geological and assay data using Leapfrog MiningTM software (Leapfrog Mining) early in the exploration stage of a project. Three-dimensional representations of geometallurgical data can be created directly using primary additive data, as well as from proxies of non-additive parameters. Resource block models can be updated continually with new information as the project passes from exploration through production. In addition, the software allows for 3D visualisation of the characteristics of the materials that will eventually constitute the mill concentrate, tailings, and waste. This study emphasises the critical importance of establishing a systematic geometallurgical approach early in the exploration stage that can be continuously updated with new information throughout the life of the project. In the example that we present, sulfide ore that had otherwise been identified as high-value (in the absence of geochemical and metallurgical data) turns out to be low-value ore due to concentrate penalties and increased environmental impact.

Sep 5, 2011

By L Lorenzen

The selection of samples for metallurgical test work is generally focused on providing geologically representative samples. One of the major problems experienced with metallurgical test work using drill hole data, is the small sample sets selected for metallurgical testing. Major process and financial decisions are being made from results obtained using snapshot samples. Snapshot samples means that samples are basically selected only by grade and spatial location with the assumption that the rest of the orebody or portion of orebody is similar and will respond metallurgically the same way – ie the assumption is that the mineralogy and lithology are basically the same as the test sample. From the test work, algorithms were usually developed and used to describe throughput and recovery using some explanatory variables including grade (mineral chemistry) and ore hardness. These algorithms were then used in the recovery models to determine tonnes produced and plant throughput. However, the metallurgical parameters are a function of many geological factors including grade, lithology, alteration, mineralogy, spatial relationships and SG. In previous years, the limited mineralogical information available was deemed enough for the metallurgist to make decisions as to how many samples are needed to evaluate the metallurgical performance of the ore during processing, and subsequently the basis for the plant design. If the orebody is consistent and not complex or refractory (thus, relative simple lithology), then only spatial and grade samples will be needed to evaluate the throughput and recovery, so limited mineralogical information may be acceptable. There is now, however, an increasing realisation that the mineralogy and geology change, as do the metallurgical characteristics of the orebody, so limited samples may not be sufficient for effective metallurgical test work.It is the purpose of this paper to discuss the effect correct mineralogical information has on the subsequent metallurgical test work program and process selection using a case study. Factors such as type of rock, rainfall and topography are thought to influence the minerals that ultimately form in various orebodies, with the result that different orebodies often react differently during metallurgical test work programs. It is therefore important to obtain accurate mineralogical and elemental deportment data, which will also indicate where the valuable metal containing minerals are located within the orebody. The better you plan and execute your metallurgical test work program during the early phases, the more accurate the decision-making process will be during the selection phase. Improved integration between geology and metallurgy data gathering (geometallurgy) during early phases will result in more accurate predictions with regard to throughput and recovery during modelling and process. Equipment selection will be considerably simpler in later phases with early planning, with improved mine and process scheduling and subsequent net present value (NPV).

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

By D Cooke

In 2010, Kalgoorlie Consolidated Gold Mines (KCGM) was Australia’s largest gold producer, with total annual production of 788 000 ounces. Of the gold produced by KCGM in 2010, 95 per cent was extracted from the Fimiston open pit, also known as the Superpit. The Fimiston operation encompasses the historic Golden Mile, from which more than 55 million ounces of gold have been extracted from the world-class Fimiston lode style mineralisation.The operational performance of the KCGM Fimiston open pit was reviewed for the period between 2005 and 2010. Key performance metrics reviewed included mined tonnes and grade, cost per ounce of gold produced and the mine to mill reconciliation trends. The years 2007 and 2008 were characterised by a decline in performance against key performance metrics. The ore zones were diluted, with the excess tonnes mined resulting in lower grade and an increase in the cost per ounce. Poor reconciliation between ore mined, the ore reserve model and the grade control polygons was also observed.A series of improvement initiatives were implemented in late 2008. As a result of changes made to the grade control and mining practices, the site performance recovered. Unplanned dilution was reduced, which had a positive impact on mined grade. The lower tonnes combined with higher grade reduced the cost per ounce to below budgeted levels in 2009 and 2010, enhancing the profitability of the operation. The operational performance between 2005 and 2010 was compared to the turnover statistics for the KCGM geology department over the same period. Turnover accelerated between 2005 and 2007, peaking at an annualised rate of 43 per cent. With the onset of the Global Financial Crisis and the implementation of retention initiatives, turnover reduced significantly between 2008 and 2010. A strong consistent correlation was observed between the turnover trends in geology and the performance of the grade control function and resultant site metrics.The cost of turnover is known intuitively, however, rarely quantified. To evaluate the benefit of the improved performance, a detailed financial model was used to quantify the impact of the grade control function. In 2010, the headgrade exceeding budget by just four per cent contributed an additional A$24 M of value to KCGM. In contrast, if the 2008 performance had been replicated in 2010 (-9 per cent on headgrade), the result would have removed A$61 M in value from the business. The KCGM experience between 2005 and 2010 demonstrates the value of human capital in a mining operation and the importance of a high performing geology grade control team to the profitability of a mining operation.

Aug 22, 2011

By N Burgio

Block cave mines are often thought of as rock factories where high volumes of production are mined from a fixed area suggesting little requirement for geological input and control. Yet caving operations have peculiar and specific technical requirements from prefeasibility through to post-mining reconciliation. Whilst essential elements such as resource definition, geological interpretation and sampling strategies are common to all mining methods, planning for a block cave requires a different emphasis and philosophical approach in how we deal with these issues. We will examine why a geoscientist’s input is critical towards the successful planning, design and monitoring of a caving operation.

Aug 22, 2011

By C L. McIntosh

The west coast of New Zealand’s south island is host to a large number of placer goldfields that stretch from the Westport area in the north to Gillespies Beach in the south and inland to the foothills of the Southern Alps. This area is host to a large number of alluvial, glacial and strand line deposits ageing in range from Recent to Tertiary. In recent years exploration models have been developed for off shore placer deposits on the continental shelf.Gold mining on the west coast has exploited placer gold resources from the 1860s to present, and with the recent surge in the gold price the industry is currently undergoing a resurgence. The current increase in mining is dominated by small-scale mechanical operations usually involving one to two people at each site; a few larger operations also exist such as the Grey River Dredge.Mining methods range from using floating bucket dredges to small diggers. Some of the mining technologies used are similar to those employed during the great gold rushes of the 19th century. Exploration and grade control techniques can be very rudimentary often relying on previous sampling programs or wide spaced bulk sample drill holes. Some bulk sample drilling is used on the larger operations as a form of grade control, most of the small operations tend not to use any ore control sampling prior to mining. Extraction techniques relying on simple gravity based designs with riffle tables or spirals are the most commonly used. Environmental standards are very high within New Zealand and require operators to return mined areas to preagreed to landforms. The high rainfall experienced on the west coast causes challenges to operators in regard to water quality. Technical issues combined with often limited capital provide for challenges to the operations.

Aug 22, 2011

By S Pontual

The use of spectral data has traditionally been for obtaining mineral information and for mapping out alteration systems. The output from these data is typically in the form of mineral assemblages and data on relative proportions of minerals. However there is much more than just mineral information in the spectral data. Most importantly, the data allow the user to obtain vital information on mineral chemistry for some key alteration minerals. The mineral chemistry data allow the users to evaluate multiple phases of alteration of the same key mineral species, and to assess which phase is related to mineralisation. Mineral indices can be extracted from the spectral data and plotted to represent variations in the chemistry and relative proportions of key mineral phases as downhole, section and three-dimensional (3D) plots. These data can also be compared with the spatial distribution of the mineralisation by integration with geochemical and logging data. Through spatial and statistical evaluation the mineral information obtained using spectral analysis can provide vital information to assist in building alteration models and therefore improve targeting for further drilling and exploration.

Aug 22, 2011

By M Fitzgerald

Fimiston Open Pit is operated by Kalgoorlie Consolidated Gold Mines (KCGM) as a joint venture between Barrick Gold and Newmont Mining. Over 110 years of underground mining resulted in the development of nearly 3500 km of underground workings including backfilled and open stopes up to half a million tonnes in size. These voids significantly complicate blasting processes at KCGM, influencing blast movement direction and magnitude. Blast movement monitoring to improve mine reconciliations was identified as an improvement project in the business plan for the geology team in 2008. Following successful trials in 2008 to 2009, blast monitoring and translation of ore blocks were implemented as a permanent grade control program from late 2009 and is now part of the Management Operating System (MOS) of the mine. This paper presents a summary of results from the Blast Movement Monitoring (BMM®) data collected to date. Ore blocks at KCGM are translated primarily using BMM® data. This data is obtained from the BMM® system which measures the movement of a radio transmitter installed in a blast pattern from a preblast position to a post-blast position. The application of the data collected by the BMM® tends to be both mine site and blast specific. At KCGM, an average distance obtained from the BMM® data within a given shot is the primary method used for translation. In the case that BMM® data is unavailable, a theoretical movement vector established from Orica Shotplus with distances gained from the historical BMM® database is used. Data is presented in an ore loss and dilution exercise for a bench undertaken at KCGM to understand the potential effects on reconciliations. Improvements that have flowed from implementing blast monitoring include improved reconciliations when combined with other grade control improvement projects. The increase in liaison between the geology and blast personnel at KCGM has been a significant improvement gained from the implementation of the blast monitoring program. This has allowed a better understanding of blast movement and the effects on ore dilution and ore loss by the mine geology team. In addition, the discussion of blasting tie-in plans between geology and blasting personnel has been improved which leads to more favourable blasting practices that take ore movement into account.

Aug 22, 2011

By P Odins

Traditionally, stratigraphic deposits have been represented by a large number of gridded surface and product analytical models, related to each other by naming convention. This approach, though suitable for structurally simple stratigraphic deposits, does not lend itself well to addressing the complex style of stratigraphic deposits which are becoming more prevalent. In addition, the sheer number of individual gridded models involved makes it very difficult to keep a consistent audit trail of the overall deposit model. Recently, a new enhanced block model structure known as Horizon Adaptive Rectangular Prism (HARP) models was introduced to address these issues. An HARP model is specifically designed to allow stratigraphic units to be represented with virtually no loss of structural integrity, by allowing the tops and bases of the individual HARP blocks to ‘bend’ in concert with the input surfaces. By bringing together the best of grid modelling, triangulation modelling, block modelling and grade estimation, the HARP model opens up many exciting new opportunities for the stratigraphic geologist or engineer to take their model further than was previously possible. Examples of improvements in modelling methodology using a HARP model include: the ability to represent complex normal, reverse and thrust faulting; the ability to store thousands of additional analytical, geotechnical or calculated variables in each individual block; advanced visualisation, manipulation and plotting capabilities; and the ability to calculate volumes and resources against complex, three-dimensional (3D) solid pit designs. Finally, several ‘real world’ scenarios, illustrating how this advancement in modelling technology can benefit a wide range of mining professionals are detailed. These examples demonstrate how productivity gains can be made in both long-term resource modelling and short-term ‘week by week’ geological modelling as an adjunct to mine planning.

Aug 22, 2011