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By M J. Nimmo

extremely powerful tools for building predictive and generative models. ML can be used for building highly accurate regression and classification models. But without careful data science and statistical analysis, a regression or classification model could be highly biased and completely wrong – and we may not even realise. An example rock density estimation task will be used to illustrate the potential gains and possible pitfalls in using ML and AI in Mineral Resource estimation. What could possibly go wrong? Nothing! For most simple regression or classification problems, very little. For density estimation, using the global mean may be all that is possible, but the resource classification would need to reflect the uncertainty in global estimation of tonnage. Everything! From data collection and measurement, from selecting ML and AI techniques and designing the analysis workflow to our own cognitive bias. The things that could go wrong are vast and may include but not limited to asking the wrong question, not asking questions, data bias, measurement errors, insufficient data coverage, not collecting the right variables, not collecting enough observations, collecting clustered samples, unbalanced data, filtering out data without reason (outliers), clipping data (outliers), relying on automatic feature selection, including irrelevant variables, excluding relevant variables, geological interpretation, changing context, blindly following best practice, splitting small data sets, subsetting large data sets, using the wrong tool for the task, insufficient budget to complete the analysis, not allowing enough time for testing and experimentation (the science), focusing too much on building the model (the engineering), not learning from the data, our assumptions, our preconceptions, and our skill. This paper will explore the question of how training regression models to predict rock density are affected by gaps in the data (missing observations and missing variables). What could possibly go wrong?

May 24, 2023

By W Patton

Investment decisions in the mineral resources sector are made on the basis of an assessment of the economic potential for a mineral deposit. Given the supporting information for the location, scale, and quantity of the commodity of interest, miners and investors aim to make risk-informed decisions when considering further investment. The objective of resource classification is the synthesis of various types and quantities of geologic information used in resource model construction to assign confidence categories to the estimated mineral resources, which can then be used to support informed economic decisions. In this paper a risk-based classification scheme for iron ore mineral resources using conditional simulations is presented. The method involves categorisation of each block according to a confidence level, considering expected recoveries from a kriging model over mining production volumes. The realisations of the simulated model are used to assign risk-based confidence categories to zones of the ore deposit. One motivation for this work is to assess the sensitivity of the scheme to geologic uncertainties. Specifically, that which uncertainty in the layout of mineralising alterations and weathering overprints influence variabilities of associated rock properties and the likelihood of obtaining expected ore recoveries. It is shown these risks have material implications for a project’s economic potential. How the scheme can be extended to the grade control context and the value subsequent grade control activities is also quantified.

May 24, 2023

By L Bertoss, D Carvalho

In the last decade, the use of advanced implicit modelling software/algorithms in mineral resource workflows has become a best practice in the mining industry. In most cases, these workflows can easily replicate the level of detail of explicitly modelled geometries, even for very complex deposits. These techniques are flexible and can produce fast results since software is responsible for generating 3D surfaces and solids based on geological and interpretation data. Nevertheless, depending on how unique and complex some deposits are, intense manual editing and input are required to honour the geological context and produce the desired outcome. The current geological modelling workflow at George Fisher Mine (GFM) is based on the semi-automatic built-in tool that generates stratigraphic sequences (stacked lithology layers) based on logging and mapping data. This tool (stratigraphic sequence from Leapfrog Geo™) works for the majority of areas in the mine but fails to capture observed mineralisation pinch-outs in operational areas and at extrapolated boundaries of the deposit. These pinch-outs have the potential to negatively impact grade forecasts if not resolved manually. Using built-in software tools only, the alternative for a more accurate model is to generate each individual layer and adding tens of thousands of manual interpretation points to honour pinch-outs (based on the amount of existing drilling in the mine and number of layers). This approach would require an estimated minimum of two to four years of a full-time employee (FTE) to be exhaustively completed. This study presents an automated approach to generate pinch-out points using a set of Python scripts that only take a few minutes to run. The newly generated points are then combined into the commercial software workflow as interpretation points, honouring observed pinch-outs. The remarkable time difference between approaches removes chance of manual errors and allows mine geologists to spend valuable time in validation and testing of scenarios, ensuring they are more representative of the deposit’s geology.

May 24, 2023

By H Arvidson, V Chamberlain, R Marinho, B Parsons, T Rowl, M Noppé, M Mattera

With the introduction of CRIRSCO (2019) based mineral asset disclosure, Regulation S-K part 1300, 2019 (S-K1300) by the United States (US) Securities and Exchange Commission (SEC), applicable to companies listed on the New York Stock Exchange from 2021, Mineral Resource is now required to be reported Exclusive of Mineral Reserve, referred to here as ‘Exclusive Mineral Resource’ (EMR). EMR is equivalent to reporting Mineral Resource additional to Mineral Reserve, which has been permitted in jurisdictions outside the US for many years. Despite this, there are no practical guidelines in the literature or relevant reporting codes and rules for Competent/Qualified Persons to refer to, which creates a risk that disclosure of EMR estimates across the industry will be inconsistent and potentially ambiguous for analysts, investors, potential investors, and other stakeholders.With the introduction of CRIRSCO (2019) based mineral asset disclosure, Regulation S-K part 1300, 2019 (S-K1300) by the United States (US) Securities and Exchange Commission (SEC), applicable to companies listed on the New York Stock Exchange from 2021, Mineral Resource is now required to be reported Exclusive of Mineral Reserve, referred to here as ‘Exclusive Mineral Resource’ (EMR). EMR is equivalent to reporting Mineral Resource additional to Mineral Reserve, which has been permitted in jurisdictions outside the US for many years. Despite this, there are no practical guidelines in the literature or relevant reporting codes and rules for Competent/Qualified Persons to refer to, which creates a risk that disclosure of EMR estimates across the industry will be inconsistent and potentially ambiguous for analysts, investors, potential investors, and other stakeholders.

May 24, 2023

By I Sucholutsky, D M. First, D Mogilny, F Yusufali

Machine learning is creating value in all facets of the mining industry, from exploration to production. The authors provide an accessible, high-level introduction to artificial intelligence (AI), machine learning (ML), and deep learning (DL); the latter being widely recognised as one of the most powerful forms of ML. In particular, the authors will introduce deep learning models known as convolutional neural networks (CNNs), how they are applied, and the economic considerations necessary for determining when DL may be the right solution to de-risking complex block modelling problems. The authors present preliminary results from the mineral resource modelling study of the Jundee orogenic gold deposit, Yandal belt/gold province, Western Australia. The primary goal was to identify the direction and location of the narrow gold veins more accurately and demonstrate how non-linearly correlated elements are used as direct inputs to the resource model to assist with the target element’s grade prediction. This demonstrates that: (1) existing techniques for finding correlations between assayed elements do not adequately reflect the complex geology of the asset, (2) nonlinear correlations that are difficult to model as simple mathematical functions are representative of geological patterns in a deposit, and (3) non-linearly correlated assayed data, fed as inputs, increase the performance of the resource model as reconciled through blind tests. To conclude, the authors hypothesise that the patterns represented by the DL block models may be revealing the results of overprinting geological processes that generated mineral deposits. For example, the primary hydrothermal processes that deposited metals created depositional patterns that become particularly complex as a result of being overprinted, in part or in whole, by secondary physicochemical processes. This may explain why non-linear geochemical relationships have the capacity to generate more accurate block models of mineral deposits.

May 24, 2023

By A A. Latscha, D O’Connor

Increasing orebody complexity, restrictions in ground access, and longer lead times for disturbance approvals, have generated the need for the Resource Development Team within Rio Tinto Iron Ore (RTIO), to explore optimised drill design techniques that deliver resource conversion value while maximising program outcomes. In response to this challenge, the Team initiated a case study to produce an AI assisted/designed drill program using a non-machine learning AI. The case study involved the assessment and implementation of an AI generated drill program at one of RTIO’s Pilbara iron ore deposits, located in the Hamersley Province of Western Australia. The Mineral Resource is currently classified as 100 per cent Inferred. Access constraints inherent to topography, and the presence of exclusions zones have prevented adequate drilling coverage, resulting in lower resource confidence and a poor definition of the orebody extents and geometry. The drill campaign, planned in Q1 2023, needs to consider access constraints, complex topology and the limitations related to additional ground clearance, while addressing the requirement of improving orebody knowledge. The drill plan had to address two objectives. The first and main aim of drilling was to upgrade the Resource Classification to allow for possible conversion to Ore Reserve. The input for this objective was a volumetric representation of the areas currently not meeting pre-defined quantitative Resource Classification upgrade criteria. The second objective was to better define the orebody extents and geometry. For the second objective, the input to the drilling optimiser was a volumetric representation (blocks) of the areas of high uncertainty in the mineralisation/waste boundary, which were defined using an indicator conditional simulation technique. The drilling optimiser output was a range of compliant drill plans. A set of KPIs based on economic and orebody knowledge criteria were used to assess the plans and to select the most optimal drill design to be drilled in 2023. The geology and management teams were able to make better informed, objective decisions using the trade-off between cost and orebody knowledge gained. As part of the assessment, the AI generated plans were compared to our current planning practises. The AI assisted plans, when gauged against the project KPIs, showed clear and measurable improvements in both meeting project objectives and in managing operational risk.

May 24, 2023

By D Clarke, I Minniakhmetov

This paper describes the application of the SBRE framework to model the Olympic Dam deposit, one of the largest copper and uranium deposits in the world. Geostatistical simulations are the best practice for quantifying uncertainty in the mineral value chain and estimating resources. However, these simulations are currently undertaken on an ad hoc basis in the mining industry due to the high requirements in computing resources and capabilities in advanced geostatistics. The SBRE project provides a standard framework to test, build, and run conditional simulation workflows, which has been applied to the Olympic Dam deposit. Conventional geostatistical methods face challenges when modelling the Olympic Dam deposit, such as non-stationarity, non-linear correlations, and a large number of grid blocks to simulate. To address these challenges, a combination of methods was used, including projection pursuit multivariate transform, trend-residual decorrelation, pluri-Gaussian simulations, and turning-bands simulations. Various tools and applications were used to build the model, including in-house developed applications, Autovariogram software (Varify), and the RMSP python library. All computations were accomplished on the AWS cloud using the EC2-Batch service. The final set of simulations was validated using various methods, such as grade-tonnage curves, histograms, swath plots, and variogram reproductions, as well as validation using historical samples back-testing. The study highlights the importance of an automated, modular, and scalable framework in the development of conditional simulation workflows and describes the learning gained from trials of different combinations of methods and software

May 24, 2023

By C Artica

Two quantitative methods for Mineral Resource classification have been applied to a copper skarn deposit beneath a large open pit that is mining a world-class porphyry complex. A drill hole spacing study (DHSS) using the single block kriging method (SBK) and a conditional simulation method (CS) were applied to the skarn deposit and the results compared. Inputs to both processes included the proposed mining rate and stope designs to estimate the likelihood of achieving grade and metal production targets. Mineral Resource classification of the deposit considers the relative precision of tons, grade and metal predictions over quarterly and annual production periods. These relative precision estimates are calculated at 90 per cent confidence interval (CI). The SBK method represented the production period as a single large block. Kriging estimates were performed in the large block for various synthetic drill hole grids, and the estimation errors derived. The error was assumed to be normally distributed due to the large block size and quantity of samples, allowing CIs to be deduced. The CS method followed a conventional sequential Gaussian simulation workflow where grades were simulated with a 5 ft × 5 ft × 5 ft grid node spacing inside mineralisation domains. Each realisation was then averaged up to monthly production volumes represented by a combination of designed stopes, and the coefficient of variation (CV) for the averaged grade was calculated. CI on the relative precision of grade predictions were then deduced from the CVs. The two methods produced similar results; despite being calculated independently. CS results were used to report uncertainty in the proposed life-of-mine plan and highlight areas for additional drilling. SBK results were used as the basis for Mineral Resource classification as this method has been previously applied to the skarn deposit.

May 24, 2023

By H Arvidson, V Chamberlain, J Joughin, N Pollock, F Cessford, T Flitton, T Rowl

Environmental, social and governance issues (ESG) have become a defining feature in the marketplace to differentiate preferred investments. With the sustainability commitment and reporting landscape anticipated to evolve exponentially, it is now imperative to rapidly develop the approach taken by mineral companies to adequately identify and address ESG risks and opportunities in the reporting of Mineral Resource and Mineral Reserve (MRMR). MRMR cannot be effectively reported as technically and financially assured and realistically executable, if ESG is not integrated into the strategy, business model, input assumptions and process that defines and drives robust MRMR public reporting. Regulatory reporting codes across the globe are presently in various stages of being updated and modified to ensure ESG factors are appropriately covered as a key part of diligent and transparent disclosure. Security exchanges are making it clear that companies must not omit ESG risks from reports to investors. This paper discusses associated challenges for MRMR reporting and suggests avenues for improvement. Key themes include: the importance of procedure rather than prescription and checklists; how ESG criteria impact Modifying Factors and determination of Reasonable Prospects for Economic Extraction (RPEE); the need for financial models to account for material ESG commitments; the uniqueness of each mineral asset; the need for education, upskilling and accreditation of both ESG experts about MRMR reporting and equally Competent Persons in ESG matters; and the need for clear and balanced reports that meet the needs of end users. (RPEE is adopted here for simplicity and consistency, recognising that some reporting codes still include ‘Eventual’ in the term (ie RPEEE). Whilst RPEE and RPEEE are subtly different the intent is to point to the process of assessing ESG factors in determining reasonable prospects.) The principles of transparency, materiality and competence remain paramount when considering the impact of ESG matters on MRMR reporting, and this necessitates an inclusive, cohesive, and integrated approach to assessing ESG issues to ensure the necessary expertise and diligence is applied.

May 24, 2023

By S Dunham

For decades we’ve been focusing on reconciliation as a tool to validate mineral resource estimates. And for decades we’ve been misleading ourselves. The concept of reconciliation is simple. Predict, measure, compare and assess the quality of the prediction. This four-step process reflects the ideas of statistical process control and the plan-do-check-act cycle of Shewhart and Deming fame and our intuitive understanding of using system feedback to improve performance. In an ideal world where data is robust and dense this type of feedback loop can be useful. In the mining industry however, there are some fundamental flaws with the conceptual framework. At almost every stage of the process from pre-estimation through to final production the mining system is plagued by material challenges with data and measurement quality and quantity, system delays, and losses from a supposedly closed system. These issues can and do impact on the benefits sought from reconciliation. Instead of providing feedback leading to improved estimation, reconciliation can result in poor decisions, distracting investigations, and exacerbate the noise in an already noisy system. Before we can rely on reconciliation as a management tool, we need to address the multiple challenges present in our everyday practices. Some of the most critical issues are addressed in this paper.

May 24, 2023

By M Bond, R R. Hargreaves, G W. Booth

There are numerous causes of speculative mineral resource and reserve inflation. Traditionally, these are linked to issues of data accuracy or misinterpretation, the use of overly optimistic estimation parameters or assumptions, an absence of independent expert verification, or the simple pressure to increase company value, covert or otherwise. Owing to its subjective nature, classification of resources and reserves remains an opaque process. Of necessity, Competent Persons rely on personal heuristic interpretations, ideally underpinned by appropriate estimation and classification experience across commodity and deposit types. Whereas independent verification of individual resource and reserve statements remains impractical, publicly available data can be interrogated for potential category inflation, particularly when viewed with exploration expenditure statistics included in annual company reports. Abrupt changes in category tonnages and market capitalisation without concomitant expenditure can aid in outlier detection. Consequently, a comparison of common exploration and resource reporting parameters offers a basis for risk assessing classification robustness, over time. In this introductory study, an analysis of globally listed companies was undertaken over a multi-year period, examining base and precious metal projects in both preproduction and operational asset classes. From these, a series of exploratory decision tree trigger points were generated against which target companies were anonymously identified and risk-ranked.

May 24, 2023

By D Corley, J Moore, M Grant, A, W R

Resource estimates are the corner stone of technical and investment decision-making. Prior to mining, resource estimation uncertainty has the greatest potential to lead to poor investment decisions, despite a significant component of resource estimation uncertainty being unknowable at this critical stage in the mining cycle. This presents a conundrum to the resource geologist in terms of risk evaluation and resource classification. Ahead of mining, there is normally considerable focus on drill hole spacing analysis to determine the ‘optimal’ drill hole spacing, taking grade and geological continuity into account; cost-benefit analyses balance improved resource definition against cost; the law of diminishing returns and the exponential cost increases acting together as spacing is reduced. Typically, a lower limit to the drill hole spacing is identified. After completing the resource drilling to an agreed spacing, resource estimation is then undertaken. At this point it is common to attempt to bracket the resource estimation uncertainty. In many cases conditional simulation is used, based on a modelled, but largely assumed, variogram model. It is also assumed that the histogram of the available drill hole sample data is representative of the in-ground mineralisation. The purpose of this paper is not to diminish the importance of drill hole spacing and simulation studies, but rather to illuminate an important aspect of estimation uncertainty that, although previously recognised, is typically overlooked – the question of how representative the available data set is in characterising the true (but unknown) distribution of mineralisation. To do this, we lift the lid on one of OceanaGold’s former operations, the mined-out Globe Progress Mine, by resurrecting a high quality, close-spaced, reverse circulation (RC) grade control data set. The Globe Progress Mine is within the West Coast Region of New Zealand, it was closed and transitioned to rehabilitation in 2016 and is now known as the Reefton Restoration Project. The exhaustive Globe Progress grade control data was used to repeatedly ‘redrill’ the deposit by extracting 35 m × 35 m spaced subsets from the original 5 m × 5 m spaced grade control data. Utilising closely spaced grade control data removed the need for assumptions regarding short range continuity, which are necessary with forward-looking analyses that are based upon broader spaced resource drilling. The extraction process used a nearest neighbourhood algorithm, repeatedly moving the origin in 5 mE, or 5 mN increments. Individual resource estimates were then completed for each of the extracted drill hole data sets (49 in total) as well as a grade control estimate based upon the exhaustive data set. Whilst the data unpinning each of the 49 estimates changed, the geological assumptions, variography, and modelling parameters remained constant. This approach was taken to isolate the impact of changing the input data. The 49 estimates were then compared against each other and the grade control estimate. The mean of all 49 sensitivity estimates was close to that of the grade control estimate in terms of contained gold, tonnes and grade. Whilst it is acknowledged that the grade control estimate itself is subject to some degree of estimation uncertainty, the close match between the average of the 49 estimates and the grade control estimate suggests that the resource estimation methodology is reasonable and appropriate. Whilst this comparison is important, the focus of this study is on the component of estimation uncertainty related to the underlying data, and this is reflected in the spread across the estimates. The spread across the 49 global estimates (highest to lowest) for this particular Mineral Resource Estimation Conference 2023 | Perth, Australia | 24–25 May 2023 142 case study was found to be significant (approximately 20 per cent in grade and metal) and that is attributable solely to the underlying data. This exercise quantifies a component of the estimation uncertainty that is inherent to all drill hole data and is distinct from the uncertainties associated with modelling methodology choices, sample and subsample quality and drill hole spacing-related interpolation uncertainty. Importantly, this uncertainty is unknowable prior to mining; we can only directly compare the resource drill hole data against grade control data after mining has taken place. For the case study, approximately 65 per cent of the estimates fell within 5 per cent of the grade control estimate for contained-gold, suggesting that for many projects the histogram of drill hole data is unlikely to differ noticeably from that of the in-ground resource. However, about 15 per cent of the estimates in this study differed by more than 7.5 per cent, suggesting that a not-insignificant proportion of estimates will be materially compromised. Whether or not the available drill hole data is representative comes down to the ‘luck of the draw’ and cannot be known at the time of resource estimation. Given the challenge of attempting to evaluate forward-looking estimation uncertainty, a component of which that can only be quantified retrospectively, what should we do as resource geologists? What are the implications for risk evaluation, resource classification and reconciliation? Furthermore, without rigorous post-mining data checks, resource geologists may conflate suboptimal modelling with the shortcomings of the underlying data. An example of this problem is discussed in the following section.

May 24, 2023

By P Dowd, A Adeli, C Xu, X Emery

Geometallurgical modelling is increasingly being incorporated into mineral resource modelling and estimation as a means of increasing efficiency, decreasing operating costs and reducing risk in mining operations. Traditional geostatistical workflows for geometallurgical modelling face several difficulties, in particular, related to the non-additivity of process response variables, to highly heterotopic data sets and/or to preferential sampling designs, all of which may explain why most attempts to estimate geometallurgical response variables from assay data and mineralogy are still based on statistics and machine learning. The aim of the work presented here is to demonstrate the applicability of geostatistics to estimate copper recovery from copper sulfide ores at the Prominent Hill Iron Oxide Copper-Gold (IOCG) deposit in Australia. As recovery is non-additive, the preferred workflow is the joint estimation of the masses of metal in the feed and in the concentrate, both of which are additive variables. While the mass of metal in the feed can be estimated from abundant online assay analyses, the mass of metal in the concentrate can only be estimated from a very limited number of laboratory-scale batch flotation tests. Traditional approaches to estimating the masses of metal in the feed and in the concentrate, such as simple and ordinary co-kriging, are described and discussed. A modified version of co-kriging is introduced, which incorporates linear relationships between the mean values of the input variables, in addition to a spatial correlation model. Co-kriging with related means is shown to outperform traditional simple and ordinary co-kriging in terms of precision, accuracy and consistency of the estimates, and offers a practical and efficient tool to deal with highly heterotopic and preferential sampling designs.

May 24, 2023

By J Levett, O Rondon, I Glacken

Reporting Codes are not prescriptive on methodologies to report or classify Mineral Resource Estimation results, but the assessment of risk and uncertainty is required, and is likely to be increasingly important in future Code updates. The 2012 JORC Code indicates that relative confidence in tonnage-grade estimations should be considered for classification, and that there should be a discussion of relative accuracy and confidence levels in the Mineral Resource Estimate. These considerations are also relevant to the drill hole spacing required for various classification categories, along with other geological and data quality criteria. This paper summarises the theory, principles and current practice of various drill hole spacing analysis techniques for both achieving a desired level of resource categorisation and for optimising grade control or resource drill-out costs. The authors provide practitioners with a guide in their use to assess uncertainty and relative accuracy, as illustrated by a number of case studies. While all drill hole spacing studies rely on the assumption of consistency of the continuity model from current to future drilling, the range of methods available provide much more objective outcomes than previously adopted ‘rules-of-thumb’.

May 24, 2023

By A Jewbali, L Allen

At Newmont Corporation drill hole spacing studies are done to support the business in understanding the cost of collecting additional information (with a specific focus on drill hole sampling density) versus the value of additional drilling in terms of resource risk reduction. For the last 20 years drill hole spacing studies have been executed in an effort to answer the following question: What spacing should the deposit be drilled at to achieve a certain level of resource confidence so that when mining occurs the resource model delivers as predicted ie what is the optimal sampling density? The optimal sampling density supports a resource model that provides a sound foundation for mine planning purposes and resource/reserve reporting. At Newmont Corporation drill hole spacing studies are now a requisite for understanding drill data density needs for new deposits and near mine expansions. This paper details the evolution of drill hole spacing studies at Newmont Corporation in terms of methodology and usage.

May 24, 2023

By D Clarke, I Minniakhmetov

The Olympic Dam deposit is truly a world-class IOCG-Ag deposit, the world’s fourth largest deposit of copper and the world’s largest known single uranium deposit. Traditional and non-traditional methods have been used to accurately model the geology and mineralisation of the Olympic Dam deposit. Conditional simulation models have long been used as the most robust method for estimating risk and uncertainty in mine planning decisions at other mines around Australia and the world. There are many challenges associated with simulating the Olympic Dam deposit at a selective mining unit size sufficient for underground mine planning purposes. These include the size of the deposit, resulting in hundreds of millions of grid blocks; the disseminated nature of the zoned sulfide mineralisation. And lastly the direct relationship of the relative abundances of the sulfide mineral species that are crucial to the Olympic Dam smelter performance. Several geostatistical techniques have been utilised in the workflow for the Olympic Dam Conditional Simulation model to address these challenges. The key techniques are: • Projection Pursuit Multivariate Transform to de-correlate the main elements before simulation. • Pluri-Gaussian implicit boundary simulation method to simulate the mineralisation domains. • Spectral simulation method of de-trended and de-correlated attributes with account of local varying variance. The resulting conditional simulation models have been validated against histograms, grade-tonnage curves, cross-plots and assessed for variogram reproduction. This paper presents how the final conditional simulation models compare to benchmarking models created using alternate estimation techniques of ordinary kriging and local uniform conditioning. It also presents a validation process using historical samples to test final estimate accuracy of the simulation models.

May 24, 2023

By M P. Murphy, C M. D Barton

When developing a new underground mine, the geological interpretation that guides the mineral resource is crucial to the preparation of a reliable ore reserve. In preparing a robust geological model to underpin a resource estimate, practitioners must weigh the comparative risk of accepting a relatively wide-spaced drill hole pattern, versus a more close-spaced pattern that ensures a high local geological confidence for mine planning but may have an exponentially increased time and monetary cost. This paper details the authors’ investigations into the geological uncertainty of IGO’s Odysseus Deposit, which is approximately 645 km north-east of Perth, Western Australia. At Odysseus, the komatiitic nickel sulfide mineralisation is irregularly cross-cut by nickel-barren pegmatites that have intruded and offset its thick ultramafic orebodies. These pegmatites generate significant uncertainty in the precision of Odysseus’ resource volume and tonnage estimates, as well as the subsequent quantification of pegmatite mining dilution in its ore reserve. To quantify the geological uncertainty in the pegmatite interpretations, IGO’s mineral resource practitioners prepared several geological risk studies over the Odysseus North lode, which is the lode most affected by the pegmatites. These studies included categorical conditional simulation of the pegmatites, preparing implicit geological models with alternative algorithms, trialling a machine learning application to prepare a maximum likelihood geological model, and finally assessing the early results of the pre-production close-spaced drilling to these different models. To assess the precision of the different single-outcome models, the authors’ compared the estimated pegmatite content of each model to the range of possibilities modelled by categorical conditional simulation within the planned stopes at Odysseus North. An estimation confidence score for each modelling method was then prepared for each alternative single-outcome modelling method. From these studies IGO concluded that a close-spaced drill pattern is essential at Odysseus, supporting the view of the Competent Person in 2017 who proposed a 25 m drill spacing in areas affected by pegmatite which had been partially classified as Inferred.

May 24, 2023

By D Hope, D K. Mukhopadhyay, J D. Harvey

South32 (S32), a globally diversified mining and metals company, is listed on securities exchanges around the world (ASX, JSE, LSE), with its primary listing being on the Australian Securities Exchange (ASX). Chapter 5 of the ASX Listing Rules (Australian Securities Exchange (ASX), 2019b) requires a mining entity to provide a summary of governance arrangements and internal controls with respect to its estimation processes, estimates and reporting of Mineral Resources and Ore Reserves annually. As a global company, it is also important to design and implement all governance processes and internal controls which can be used consistently across the organisation. The three lines model (previously known as the ‘three lines of defense’ model) is employed across South32 to manage risk where the first line (Practitioners) designs, implements and executes processes and controls to manage risk; the second line (Stewardship) identifies material gaps and improvement opportunities in the first line by monitoring conformance with the group requirements, legislative and regulatory obligations. Second line also assists with the development and improvement of core fundamentals based on industry good practise. The third line (Group Assurance) provides independent and objective assurance over the Group’s system of risk management and control. The estimation of resources and reserves for most of the operations within South32 is managed by the Planning function. The estimation process is part of the annual Life of Operation Plan (LoOP). The LoOP identifies and assesses the strategic alternatives for the development of an operation and recommends the preferred pathway based on value and risk. The LoOP starts with opportunity framing and the updated resources as an input and ends with the short-term plan and annual budget (the outputs). Ore Reserves estimates are one of the outputs from the LoOP. All the inputs and outputs follow internal standards and procedures which includes peer review and performance reconciliation. Stewardship (Second line) which is a centralised function has standards in place to ensure compliance to internal controls and external regulatory requirements. This includes assessment of the competence of Competent Persons, technical reviews, independent audits and training in the relevant chapters of the ASX Listing Rules, Guidance Notes and Appendices. Group Assurance (Third line) which reports to the Risk and Audit Committee of the Board of Directors ensures the design and operating effectiveness of the processes, standards and procedures associated with the estimation and reporting of resources and reserves. While the level of scrutiny has a different intent for each line in the three lines model, the overall purpose is to achieve a consistent outcome across the organisation

May 24, 2023

By G Zhang, I Glacken

Multiple indicator kriging (MIK) has been used in the minerals industry for some decades. As one of the non-linear estimation methodologies, MIK has advantages related to resolving multiple or mixed populations, high variability data and strongly skewed distributions. Applying the MIK methodology requires a lot more effort when compared to other non-linear methodologies, for example localised uniform conditioning. This is due not only to the variogram modelling for multiple indicators, but the validation is also very important because the panel model is fundamental for follow-up work, including the generation of recoverable resources at the local or SMU scale. Apart from this, only a few reported case studies of MIK have documented post-processing of the estimated modelled distribution, representing the probability distribution at point scale at un-sampled locations. The MIK post processing should include the change of support from the point scale to the panel scale, and then the extraction of quantile values from the panel conditional cumulative distribution function (CCDF) for localisation of grades into the SMUs within each panel, based on a ranking estimate marking the SMUs from high to low. In this paper, we present a case study on a gold deposit with the MIK method with full-post processing implemented for recoverable resources. The MIK point estimate was carried out with an indirect lognormal change of support from point to panel scale, and localisation was done with a custom script. The localised uniform conditioning (LUC) method was also applied for comparison purposes. The comparison shows that MIK has advantages compared to LUC when comes to capturing the high-grades, especially when mixed or varying anisotropy is present within the mineralisation.

May 24, 2023

By D A. Sims

This paper outlines the contributing factors to a series of erroneous Mineral Resource estimates which led to considerable loss, including significant reduction of mine life and consequently a large write-down of asset value. The error prompted divestment of the mineral asset in the short-term by the owners, a major mining company. There were multiple and complex contributions to the result: • The mine was undergoing a transition in the deposit geology with increasing depth that was not accommodated in the models. Early indications of the geological transition in initial drilling were generally unrecognised in resource modelling. The shortcomings of the models became apparent with increasing infill drilling and subsequent production reconciliation. • There was a lack of recognition of, and confidence in, site-based input and skills by corporate technical management. The site had a reputation internally for over-delivery to plan early in its life and that supported the anticipation of ongoing metal delivery into the mine plan by management. • A corporate lack of confidence in site being able to adequately domain the mineralisation led to the use of a CIK domaining process, rather than one which was geologically based. This domaining process led to gross over-statement of mineralisation trends reflected in the subsequent OK estimate. • Timelines on the outsourced estimation process were so short that validation was not possible. Obvious errors in the estimate versus data were not able to be used to correct the issues and the site’s Competent Person sign-off occurred without adequate checks. This case study is presented to reinforce the concept that Mineral Resource estimates are not solely based on the ability of the Competent Person to satisfactorily guide the process. There are usually multiple complex issues in the estimation process which need to be recognised and considered in judging reconciliations of model to actual.

May 24, 2023