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By K Gardner, R Fraval, V Singh

Using new technology to create optimised infill drill programs in underground or open pit settings, it is possible to quantify the value of a drill program. The computer designed programs are optimised to uplift the most amount of resource possible into the specified resource classification, while honouring geological, spatial, equipment and budgetary constraints. When designing ‘by hand’ balancing all these factors is a time-consuming effort with no real way to determine metrics on ‘how good’ a program will be. The optimisation considers many factors allowing geologists to apply critical thinking and domain knowledge to constrain the optimisation, letting the computer do the heavy lifting of determining hole placement. Once programs have been created, they can be integrated into a production schedule. This is a critical step in determining the correct sequencing and balancing of resource availability and seeing how a program will interoperate with other tasks in the mine. For example, if drill drives are a requirement for part of the program, will they be developed in a time frame that is useful for the goals of the program? Do we risk drilling infill holes too late to add value? Do we put production at risk by not classifying parts of the model ahead of time, thus adding to uncertainty? Using software to assist with optimising and scheduling of drilling is a useful technique to save the geologists from the mundanity of manually designing drill programs and puts metrics around the effectiveness of a program and has the potential to add quantifiable value to a program. Examples will be presented that demonstrate the effective use of drill hole optimisation, comparing manual designs with optimised designs, as well as the examples that show the effective use of scheduling.

Mar 22, 2022

By O Otgonbayar, B Bayarmaa, D Batbyamba, S Amar-Amgalan, K H. Natsagdorj, J Nomin-Erdene, B Boldkhuu, T S. Sainzaya

The Argyle deposit in the Kimberley region of Western Australia was the world’s largest accumulation of natural diamonds. Following its discovery in 1979, the mining of associated alluvial diamonds commenced in 1983, followed by the pipe in late 1985. Over the life of the mine to its closure in November 2020, Argyle produced approximately 900 million carats of diamonds. Argyle diamonds are distinctive in having a predominance of brown colours and rare highly-prized pink diamonds, which are a signature of the deposit. The 3D geological model developed from mining and deep drilling shows that Argyle is a composite pipe formed by the coalescence of several steep-sided diatremes, each with their own feeder zones at depth, that were erupted along a NNE-trending fault. The ~1.2 Ga Argyle pipe is comprised of diamondiferous olivine lamproite volcaniclastic rocks and intruded by sparse olivine lamproite dykes. The volcaniclastic rocks are predominantly comprised of unconsolidated quartz-rich lapilli and coarse ash deposits formed by numerous phreatomagmatic eruptions of olivine lamproite magma through Mesoproterozoic water-rich sands and silts. Post-emplacement deformation has tilted the orebody 30° to the north and regional strike-slip faulting has offset, segmented, and elongated the orebody. Recent significant modifications of the geological model resulted from underground block cave operations, producing several critical lessons learnt. Diamond grade and quality reconciliation issues warranted a geological investigation which resulted in the recognition of new volcaniclastic olivine lamproite domains with a broad range of diamond grades, including some very high-grade units. The new lamproite domains had a significant effect on the block cave mining and resultant recovery of diamonds. The new findings enable a more coherent and detailed geological understanding of the volcanic emplacement and paragenetic sequence of the Argyle AK1 pipe. Structural modelling of the deposit also played a major role in the block cave mining tonnage recoveries and daily operations. The re-examination of the geology of the Argyle pipe and the results of this new information reinforces the need for ongoing robust geological work both in the early stages of mining a deposit and continued during the life of the mine.

Mar 22, 2022

By O Dominguez

Significant value can be added to ore supplied from the mine to the mill by measuring its quality in real time and performing grade control additional to that within the mine. Responsibility for ore quality between the mine and mill stockpiles is not always clear due to operational siloes so geologists and mining engineers need to be involved collaboratively with metallurgists to optimise ore value prior to processing. Sensors, such as the GEOSCAN-M which utilises high specification Prompt Gamma Neutron Activation Analysis (PGNAA), that measure conveyed ore flow representatively (continuous and fully penetrative) and in real time have been used in the minerals sector since 2003 and have been successfully applied in multiple commodities. Quality can be measured within the mine where material is conveyed underground or in-pit. Bulk ore sorting is used to divert increments of waste from the ore stream to upgrade material sent to the crushed ore stockpile (COS) as well as to create run-of-mine (ROM) stockpiles of different qualities to maximise blending opportunities and accommodate processing priorities, eg blending out deleterious content. The real time composition measurement can be combined with moisture and fragmentation analysis data for improved ore type characterisation, to control ore blending, for feedback to the mining schedule and block model reconciliation, and feed forward control for grinding and processing operations. The paper discusses various parameters measured and derived in existing operations that are used to maximise coarse waste rejection and metal recovery, optimise plant feed quality consistency, and reduce consumables; power, grinding media, water, reagents, and minimise fine tailings generation. Innovative geometallurgy applications are also discussed.

Mar 22, 2022

By P Willems, J B. Crow, R Dale

Open pit mining commenced at Lihir Island in 1997 and is undertaken using a conventional drill, blast, load, haul methodology. Unique challenges at the operation include an active geothermal system, a well-developed advanced argillic blanket in the upper portions of the orebody and high rainfall. Grade control is effective utilising routine blast cone sampling. While this provides ‘just in time’ grade information for mill blending, there are opportunities to improve scheduling of mill feed, based on material classification of clay types and sulfur grades. The nature of the clay can impact on the handling properties while the sulfur grade is an important guide for autoclave blending, required for refractory gold. Drilling below the pit floor introduces hazards from the identified geothermal field. A trial Reverse Circulation (RC) program was implemented with the aim of defining four benches below the active mining bench. A comprehensive risk analysis, including the geothermal environment, was undertaken to design a drill collaring procedure with pressure diversion equipment. Operator exposure was minimised through ‘nil on foot’ principles with the driller located in an onboard cab, utilising remote rod handling. Sampling personnel operate in an elevated platform with access to protective covering. Samples from blastholes and the RC holes are processed at two site laboratories. One provides conventional Au fire assays with additional Infrared Combustion (Leco style) S analysis. The second laboratory collects material classification details from portable XRF (pXRF), near infra-red spectrometry (ASD) and Hardness Indicator Testing (HIT). An existing workflow was updated by the site to deal with the additional samples and ensure production ‘turn around times’ were retained for short-term planning. This required some changes to drying temperatures, compositing and screen sizing for the material classification. All results from the two laboratories are loaded into a centralised acQuire database for estimation into a single block model that informs the 13 week medium term planning (MTP) schedule. This paper describes some of the logistical issues faced when drilling in a geothermal area. It also discusses the selection and commissioning Quality Assurance of the sampling equipment and drill rig. Integration of the resulting assay data with existing resource models is also explained.

Mar 22, 2022

By D Lucato, B Haase, R Saha, S Limberger-Forte, J Lishman

Cadia East Mining operations have employed geometallurgical techniques to advance the understanding of the in situ rock hardness variability across existing Panel Caves (PC). Geometallurgy advances the understanding of variability across an orebody through the integration of geology, mineralogy and processing response and therefore enhances spatial mapping of comminution or separation properties. Spatially extensive geometallurgical measurements are necessary so that continuous rock properties can be generated and used for the development of comminution and processing domains using geostatistical modelling. Standard testing protocol across geological and mineralogical domains is vital to understand the variability in deposit wide comminution and separation properties. The high cost and large sample requirements associated with metallurgical test work typically results in insufficient data density and distribution, making it difficult to model and map deposit scale variability. Quantitative hardness proxy tools are useful to measure in situ rock hardness, however this data is typically either sparse or missing within advanced mining operations. In recent years, mineralogy and multi-element assay data has often been collected as part of the standard geological data set and is generally coincident with metallurgical test work. In this paper relationships between mineralogy and comminution parameters have been applied to multi-element assay data with coincident measured Bond Work Index (BWi) data to generate a predicted BWi using linear regression. The predicted BWi is intended to supplement existing measured BWi data to enable better extrapolation across the deposit. Geostatistical Conditional Simulation methods have then used the predicted and measured BWi data to produce a BWi variable as a new input to the resource model. The simulated BWi can be used as an input for specific energy models to predict required grinding energy consumption across a deposit.

Mar 22, 2022

By E Philippa, J Vitanza, C Hawtin, B Miller

B Miller , E Philippa , C Hawtin and J Vitanza

Mar 22, 2022

By P J. Willems, B J. Junor

Blackwater Mine is in the central-southern Bowen Basin, immediately south of the township of Blackwater on the Capricorn Highway. The current open cut operation extracts coal from three main seams, designated Top, Middle and Lower, in the Late Permian Rangal Coal Measures cropping out on the eastern flank of the Comet Ridge. Complex geological structures, dominated by E-W normal faults and N-S reverse faults, have significant detrimental effects on coal mining operations. Historical exploration before 2010 involved several types of drilling only to understand structure, coal quality and washability. Hence, although drilling data was combined with limited pit mapping and observation, unknown structures were still often encountered during excavations and led to mining surprises. In 2010 BHP completed 200-line km of closely spaced 2D seismic survey to capture gross and detailed structures followed by fit for purpose structural drilling to confirm major structures in the northern part of the mine. In 2018 a high density 9.2 km2 3D seismic survey was completed in the southern part of the mine. Subsequently, a 17.2 km2 survey was completed in 2020 to cover the northern active mining area. It allowed geoscientists to successfully map previously unknown major faults and seam splits, which could potentially impact the mineability of the area. It is important to note, that faults interpreted using modern high-density 3D data can be interpreted with dramatically higher confidence in the fault dip and azimuth orientation in comparison to the legacy 2D data. Along with targeted drilling, pit mapping and structural interpretation, the outcome adds value to mine planning and operations. This paper outlines recent successful seismic 3D data acquisition and interpretation. In detail, the paper presents case studies on how the data can be incorporated into a structural model to improve geological knowledge, enable geotechnical assessment, and support safe and efficient coal mining in a large and structurally complex mine.

Mar 22, 2022

By M Haest, S Sandler, R Botha, H Lang, K Hume

Benchmark practices in the mining industry include the establishment of Quality Programs — Quality Assurance and Quality Control (QAQC) — to monitor the correct execution of sampling protocols and control each stage of the ‘Sample Cycle’: Sample Collection, Preparation (comminution) and Analysis. QAQC reports commonly include statistical-numerical results that quantify performance of QAQC controls (field duplicates, preparation duplicates, blanks, standards etc). Graphics such as scatter plots, QQ plots, histograms and cumulative frequencies are used to represent the results. Statistical values including relative difference, absolute difference, relative variance, averages, T-test, and Zscores are used to quantitatively express the relationship between duplicate pairs ... but is an effective quality program just a statistical exercise? This paper aims to highlight the concept of ‘Quality Management’ (QM) as the precursor of continuous monitoring (ideally in real time) of QAQC results on Exploration, Production, Processing and Port samples, determined by trend analysis performed on a time and/or grades basis. Currently, there is a paradigm in the process of quality assurance that if individual data points fall within a predetermined acceptance limit then they are ‘acceptable’ and therefore suitable for informing operational and investment decisions. However, what is stated with respect to QM is that sometimes results found within the acceptance limits can indeed be internally biased, or show material deviations over a period of time, thereby impacting reconciliation results and/or potentially creating discrepancies with the final buyers. QM refers to the proactive detection of these ‘anomalous tendencies’, the approach and communication with the source of the information, the understanding of the reasons that cause the deficiencies, and the generation of agreed action plans to remediate the situation and ensure the consistency of results, in other words: continuous improvement on proactive manners.

Mar 22, 2022

By A Kleinitz, J Halilovic, K Trappitt

Mine operators use grade control to determine the destination of each block of material in the mine. In polymetallic mines, grades are best expressed in dollars: the Net Smelter Return, NSR, per tonne. To estimate NSRs, geologists working on grade control develop models for each geometallurgical domain in the mine. These models may incorporate observations on the mineralogy and grain sizes of the material being examined. The site’s mill operators contribute to the models by providing geologists with statistical analyses that relate assay results and mineralogical compositions to metallurgical parameters, such as the rates of recovery of each economic metal to each of the concentrates produced, and the compositions of those concentrates. The team responsible for sales and marketing of the site’s products contributes to the NSR models by providing details of the costs of shipping concentrates to smelters and refineries; and by providing figures that are specified in the smelter contracts, such as the payability of the metals in each concentrate and the fees charged by the smelter. The mill operators also provide, for the NSR models, equations to estimate the rates of recovery of deleterious materials to each concentrate. Deleterious materials comprise mostly halogens and amphoteric elements, both of which reduce the efficiency of smelters. Furthermore, deleterious materials such as antimony and arsenic may be poisonous to people and to the environment. The sales and marketing team provide specifics of the penalties that smelters impose upon deleterious materials. These penalties may reduce NSRs by tens of percent, possibly to levels below the cut-off grade. Thus, in the blocks that are subject to grade control, mine geologists monitor not only the concentrations of economic metals, but also the presence and abundance of deleterious materials. The preparation and maintenance of models, to estimate NSRs and to assess changes in the levels of potentially toxic materials to be mined, requires close and continuing consultation between corporate departments. There must be strong communication of mine geologists with the mill operators; with the people who market the mine’s concentrates; and, should there be a risk of encountering toxic levels of deleterious materials, with the mine’s health, safety and environmental officers. This co-operation could require radical changes in the structure of many organisations. However, experience suggests that such changes can significantly improve the profitability of a mine.

Mar 22, 2022

By M Forbes, R Jeuken

Brownfields geoscience measurement such as drilling and surface geophysics can be costly and it is often difficult to weigh the cost of data collection and processing against the value it unlocks at the time of extraction. This makes it difficult to prioritise geoscience expenditure and find the optimum intensity of geoscience activity across competing assets. There are multiple options for managing resource uncertainty and spatial variability through mine planning and resource engineering such as varying marketing strategy, adjusting stockpile or other infrastructure design and product blending. Consideration of these alongside increased data collection further complicates finding a comparable value of geoscience programs to determine the right blend of geoscience measurement and engineering controls. Concepts similar to Value of Information (VOI) analysis which is applied to strategic planning can be used to estimate the value of geoscience measurement and pit sequencing at tactical planning and operational timescales. This involves the quantification of resource uncertainty and prediction of how it will affect resource extraction. This paper outlines this process and demonstrates its use to prioritise geoscience at BHP Coal Assets. The value of infill drill programs in enabling operational stability is quantified and compared the value of engineering controls like increased stockpile capacity. This study supports that idea that the application of VOI methods to bulk mining at tactical time horizons has potential to inform geoscience planning and resource engineering not just separately, but as part of a unified strategy for managing resource uncertainty.

Mar 22, 2022

By S A. McManus

With the advances in software speed and capability, many of us now are running multiple scenarios on entire models rather than just testing small areas or a few blocks. In the authors experience both the geological and geostatistical academic theory and our rules of thumb, often prove un-useable or incorrect in the real world we work in. The only way to truly validate and tune our models is to complete the entire model and ‘see if it works’. If it lacks some property we were expecting, then we need to find out why and/or run different scenarios to see what changes. This is not always possible with tight project timelines but we find ourselves doing more and more of this sort of thing, and in so doing, understand that every deposit is different and requires its own ‘rules’ to get valid and useful results. For the purposes of this paper, this process is termed Empirical Geostatistics. Take Clayton Deutsch’s ‘all realisations all of the time’ concept a step back. Before you even think of simulating, test the alternate realisations generated by alternate parameters such as different domaining, varying search neighbourhood parameters etc.

Mar 22, 2022

By A Purton, C Taylor, E Conroy

The spatial modelling of geometallurgical domains is an important step in the characterisation of an ore deposit allowing for the quantification of various rock types, their differing rock properties and the overall value of a project. The traditional approach to modelling such features is through the use of deterministic models, where a single estimate of the extents and structure of the ore deposit is mapped out by geologists based on available data and their interpretation of the geological processes. Any uncertainty in the layout of different domains is not accounted for and heterogeneities of chemical and physical properties of the orebody are likely underestimated. Spatial machine learning techniques can be used to derive geometallurgical categories, or classes, from multiscale, multiresolution and high dimensional measured rock properties. Subsequently, geostatistical simulations of these classes can be applied to obtain multiple equiprobable realisations that define the layout of the geometallurgical classes at unknown locations between boreholes, or domains. In-turn these realisations can be used quantify the uncertainties in the location of boundaries between different domains. The development of a workflow for defining geometallurgical domains and later geostatistically simulating them across the Orebody H; a complex stratabound Bedded Iron Ore deposit located in Western Australia’s Pilbara region is demonstrated. This could be used to identify zones of high uncertainty where collection of additional data might help mitigate or minimise risks and in turn improve forecast production performances.

Mar 22, 2022

By D Kentwell

IGO Ltd’s (IGO) Nova Operation (Nova) is 160 km east-north-east of the town of Norseman in Western Australia (WA). Discovered by Sirius Resources NL (Sirius) in 2012 and acquired by IGO in 2015, the Nova-Bollinger Deposit (Nova-Bollinger) occurs within the Fraser Zone of the AlbanyFraser Orogen. Nova-Bollinger is a complex nickel-copper-cobalt (Ni-Cu-Co) magmatic intrusive sulfide deposit, which is characterised by high-grade folded and undulating breccia lodes that host the bulk of the payable metals. Significant sulfide mineralisation is also found in several magmatic intrusive styles including net-textured, disseminated and blebby styles. A smaller but material volume of sulfides has also been remobilised into the deposit’s footwall rocks as ‘stringer’ bands in metasediments, or as discrete massive sulfide lenses. When interpolating Mineral Resource estimate (MRE) block grades in the presence of locally varying grade connectivity trends, such as folds or undulations, the sample search neighbourhood, which is used to identify the composites subset that is used for each block estimate, should be oriented to match the local trends. In theory, the most robust method of estimation where locally varying angles (LVAs) are applied to search neighbourhoods, are techniques where the composites and the blocks to be estimated are transformed to an unfolded coordinate system (UCS). However, UCS methods can be tedious to implement and require subjective decisions to be made that cannot always be fully defended. An alternative LVA method commonly used in the industry is dynamic anisotropy (DA), which is much simpler to implement and is often assumed (but not that often demonstrated) to result in similar results to UCS-style estimation. In 2018, Nova’s Mine Geology team (MGT) estimated the grades and density of the Nova-Bollinger MRE using the UCS approach. For the 2020 MRE update, Nova’s MGT tested and compared the USC grade interpolation method with the DA approach. As applied to the Nova-Bollinger’s 2020 MRE, the comparison, advantages and disadvantages of the two estimation methods are the subject of this paper

Mar 22, 2022

By A Whaanga, W Vigor-Brown

IGO Ltd’s (IGO’s) Nova-Bollinger Deposit (Nova-Bollinger) is a magmatic nickel-copper-cobalt (NiCu-Co) sulfide deposit found 160 km east-north-east of the town of Norseman in Western Australia (WA). The deposit is hosted by the Mesoproterozoic rocks of the Fraser Zone, which is part of the Albany Fraser Orogen (AFO). Since mining commenced in 2016, IGO’s Nova Operation (Nova) has mined and processed to 31 December 2020, 5.63 Mt of ore grading 2.04 per cent Ni, 0.86 per cent Cu and 0.07 per cent Co. Nova’s mine geology team (MGT) has used commercially available implicit modelling (IM) and general-purpose resource modelling (RM) industry software systems, to prepare the 31 December 2020 (CY20) update of Nova-Bollinger’s JORC Code reportable Mineral Resource estimate (MRE). This CY20 MRE update was predominantly based on ~386 km of diamond core drilling, with the drill holes having a nominal grid spacing of 12.5 by 12.5 m throughout the mineralised zones of the deposit. Using IM tools, Nova’s MGT interpreted and modelled 22 separate domains from the drill hole logging, assaying results, and the MGT’s underground mapping. These IM closed volumes were then used to control the estimation of MRE variables into conventional industry digital block models using RM and other ancillary software systems. During 2021, a commercial machine learning (ML) software was trialled by Nova’s MGT for preparing block models of Nova-Bollinger’s MRE domains. The motivation for the trial was that the ML process could expedite modelling through the direct creation of a domain coded block model from the drill hole logging. Compared to the industry standard approaches of wireframing or IM, where the modeller usually makes many subjective choices to produce the domain model, ML offers a more objective ‘hands-off’ process for determining the connectivity of domains defined in drill holes. Additionally, the ML process provides a quantitative confidence metric associated with its output that may have utility in mine production and resource classification. In this paper, the results of the Nova-Bollinger ML modelling trial are compared to the MGT’s current workflow for MRE work. The MGT’s conclusions as to the advantages and disadvantages of the IM and ML methods for preparation of MRE domains are discussed. The ML software was also tested as a method of rapidly interpreting geology outside the principal zones of MRE interest. These testing results are also presented in this paper.

Mar 22, 2022

By C B. Smith, G L. Boxer, S W. Moss, A L. Jaques, V Lorenz, M J. Rayner

The Ernest Henry Iron Oxide Copper Gold deposit is hosted in a large dilation between two moderately dipping shear zones found in the footwall and hanging wall of the deposit. These shears are controlled by a large diorite intrusion to the north-east and south-west of the deposit, formed ~1660 Ma. A shear zone dubbed the ‘Interlens’ uncovered through underground workings truncates the main orebody into two lenses, and is hypothesized to have formed in conjunction with the Hanging Wall Shear Zone and Footwall Shear Zone. All three shears dip moderately to the SSE (50–140) from open cut exposures through to the current mining horizon approximately 800 m below the surface. Routine geological work and a capital drilling campaign has identified a significant flexure in these sub parallel ore bounding shears with increasing depth. A shift in shear orientation in the Interlens over approximately 100 m vertically from ~50° to the SSE to a steep, easterly trend (~85° to the east) is observed in both drill core and exposures in the underground workings. It is hypothesized the diorite intrusive to the south-west of the deposit controls the shear zone geometries at depth. The geometry of the Interlens mirrors the surface expression of the Proterozoic package of the orebounding shears to the south-west of the deposit, suggesting the surface geometries of the shear zones are consistent with depth. Additional drilling to explore the validity of this hypothesis is planned for the near future to better understand the structural controls on the Ernest Henry mineralisation with increasing depth.

Mar 22, 2022

By P C. J Blackney, I M. Glacken

Spotted Quoll is a massive sulfide nickel deposit located in the Forrestania greenstone belt in the Archaean Yilgarn block in Western Australia. Discovered in October 2007, mining has progressed from open pit to underground and currently uses longhole open stoping with paste fill as the primary stoping method. Immediately prior to 2019, the Mineral Resource estimate (MRE) was defined by a single grade domain of nickel greater than or equal to 0.8 per cent, irrespective of the host lithology, a legacy of the original ore wireframe being built using explicit methods. While a number of obvious structural offsets were honoured in this nickel grade domain, only one modelled fault was used to undertake domaining of arsenic. From 2019 onwards, implicit modelling in Leapfrog Geo™ was used to construct a mine-scale geological model, which triggered the definition of a high-grade core of massive/matrix nickel sulfides surrounded by a low-grade halo of stringer/disseminated nickel sulfides in the surrounding rock. As part of the evolution of the geological model and with the aid of Seequent Central™ to allow collaboration between site-based mine geologists, Perth-based resource geologists and Melbourne-based consultants, a detailed review of the structural mapping was carried out as additional information from underground development became available. This allowed numerous faults and shears to be identified – particularly in the lower half of the mine. These were used to define further structural domains and to refine the understanding of the nickel and arsenic grade distributions in the mineralised envelope. The increased definition to the domains provided by the geological model resulted in a significant improvement in the accuracy of the Mineral Resource estimate, as measured by reconciliation using voids of the mined stopes determined from the cavity monitoring survey (CMS).

Mar 22, 2022

By P Micale

Geological data provides the foundations for the mining value chain. It is used to underpin the modelling and planning and consequently optimise downstream business decisions. However, it is often forgotten or taken for granted. End users often assume the data to be sound without verification. Geological data can be considered a company’s number one asset. Subsequently, the storage, use and management of this data need careful curation and time invested in preserving and protecting it. An established polymetallic mine with 20 000 drill holes could have a data replacement value in excess of $500M at 2020 prices, excluding labour and salary costs. The Australasian Code for Reporting of Exploration Results, Mineral Resources and Ore Reserves (the ‘JORC Code’) provides guidelines for Public Reporting. Within the code, Table 1 requires discussion around the different types of geological data, its collection and use. What is needed is additional details regarding the validation and integrity of the geological database. A comprehensive guideline specifying minimum geological standards for geological databases has been developed. This expands on the critical points outlined in Table 1 of the JORC Code. This guideline can be used by companies where geological databases are managed in-house or used to assess these services’ outsourcing. This guideline will ensure that geological data is reliable and unadulterated and can be used with confidence. Using this guideline will assist in preserving the investment of geological data in the mining value chain

Mar 22, 2022

By R Hargreaves, T Elkington, W J. Shaw, G W. Booth

Every day, around the world, thousands of tons of ore are loaded onto ships completing the miningproduction process and starting a multimillion-dollar economic transactions process. These transactions are based on the supply of tons of a commodity with certain specifications of assays of the main element (or physical properties) and concentration of contaminants, which are finally verified and evaluated by buyers, and also are considered ‘as true’ during Reconciliation process. On Sample Stations, this ‘true’ is based on a few kilograms of sample, often representing thousands of tons, that are sent to laboratories for further sample preparation and chemical analysis, where finally assay information is obtained from a few grams of pulverised sample, and are used for further production, planning, operational and financial-marketing decisions. Different International Standards (depending on the commodity) are used to establish the methodology to be followed, to set-up and obtain samples from the Sample Stations, and the specifications are visually inspected to give an estimation on the performance of the sampling point. This paper seeks to highlight with examples, the importance of the implementation of a continuously monitored Quality Program (QAQC and QM) on Sample Stations, focused on the parameters that directly affect the quality and representativeness of the samples. By this implementation, a proactive approach and better-informed decisions can be taken for mining companies to: a) quantify/improve production and reconciliation results, and b) to supply the right specifications of shipped ore, increasing the value of the Resources. Finally, laboratory results are normally used as an indication of the quality of the information applied for the economic transaction, but it should be considered, clarified and highlighted that the laboratory will only process and analyse a sample obtained from a Sample Station, and potential problems on bias or precision may occur in stages prior to the laboratory reception. Hence the importance of the implementation of a quality assurance program that protects multi-million dollar decisions.

Mar 22, 2022

By P M. Hetherington, M P. Murphy, K E. Crook, F Pym

Resource characterisation is critical for informed decisions in both strategic and operational space. The sources of information for resource characterisation are limited by: direct measurement of resource attributes, such as grades, rock-types, mineralogy, and are available only from sparse locations, ie drill holes; or indirect geophysical measurements such as seismic, gravity or electro[1]magnetic survey. Data collection tools, methods, and processing technologies are constantly improving providing a broad spectrum of options for data collection strategies. The cost of each data survey varies from thousands to millions of dollars with the value of each additional data having diminishing return for a particular business decision. The value of information analysis provides the optimal decision on data collection strategy within a limited available budget. This work presents an example of value of information analysis for new Blast Hole Assay Tool (BHAT) and Reverse Circulation Grade Control (RCGC) data used in addition to the wider spaced exploration data. The value of information is quantified in terms of reduction of grade uncertainty, ore loss and dilution by comparing different combinations of data collection methods. Four different strategies were considered: exploration samples in addition to 50 per cent of blastholes surveyed with BHAT, exploration samples in addition to 33 per cent of blastholes surveyed with BHAT, exploration with wide-spaced RCGC data, and exploration without additional grade control. For each strategy the observed values have been simulated using advanced multivariate geostatistical simulations and compared with simulated ground truth models. The calculated uncertainty, ore loss and dilution informed the optimal data collection strategy. Opportunity to reduce sample density from the nominal 50 per cent of drill holes was evaluated.

Mar 22, 2022

By A Boucher, M Baudin, P Androszczuk, I Minniakhmetov, A Rigby, P Machado, M Wright, B Van Dijken

IGO Ltd’s (IGO’s) Nova Operation (Nova), which is 160 km east-north-east of the town of Norseman in Western Australia (WA), mines and processes nickel-copper-cobalt (Ni-Cu-Co) sulfide ore from the magmatic Nova-Bollinger Deposit (Nova-Bollinger), which is hosted by the Mesoproterozoic age high-grade metamorphic rocks of the Albany Fraser Orogen. Over its 56-month life-of-mine (LOM) operations to 31 December 2020, Nova has processed 5.63 Mt of ore grading 2.04 per cent Ni, 0.86 per cent Cu and 0.07 per cent Co from its underground mine. At the end of 2020, both the Nova-Bollinger JORC Code reportable Mineral Resource estimate (MRE) and Ore Reserve estimate (ORE) were >90 per cent classified in the highest JORC Code confidence categories of Measured Mineral Resource and Proved Ore Reserve respectively. The high JORC Code classification confidences assigned to the Nova-Bollinger MRE and ORE results from the 386 km of surface and underground diamond drill testing of the deposit to a nominal 12.5 by 12.5 m pierce point spacing in mineralisation. The close spaced high-quality drilling and bulk mining method employed at Nova has effectively removed the need for any grade control modelling outside of the MRE process. Importantly, having only the one fully drilled out deposit being mined and only the one process plant, Nova’s reconciliation process is in the fortunate situation of not being confounded by issues such as external ore sources and constant updates as new grade control information becomes available. IGO’s Nova reconciliation processes are focused on reconciliation ratios that quantify operational period differences between the MRE and ORE head grade and tonnage forecasts and the process plant head actuals. These metrics have been used to identify key reconciliation issues and have initiated several studies into the causes for specific reconciliation results, which are discussed in the paper. In late 2020, a period of sustained poor reconciliation results led to the recognition that increasing the understanding of risk between the MRE and ORE should be investigated. To assist with modelling stope level risk within the mine plan and subsequent reconciliation, an attempt was made to develop a formula to quantify ‘stope risk’ from MRE metrics such as sample spacing, kriging efficiency and so on

Mar 22, 2022