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By G R. Hancock

A common and cost-effective method for post-mining landscapes is to construct linear hillslopes with a break in the hillslope at appropriate distances to reduce hillslope length and resultant erosion risk (Figure 1). The break in slope occurs at a point that has been found by local experience to be appropriate for the materials and climate. Further enhancement of the linear hillslope may be assisted by surface treatment such as ripping (Saynor, Lowry and Boyden, 2019) (Figure 1). Firstly, ripping introduces surface roughness which breaks up flow paths reducing flow velocity thereby reducing erosion potential. The depression produced by the ripping acts as a trap which captures water, sediment and organic matter

Aug 2, 2023

By G R. Hancock, M J. Saynor, J B. C Lowry, T J. Coulthard

Landform evolution models (LEMs) can assess the effectiveness of landform designs applied to a rehabilitated landform by predicting where erosion may occur. Here, we compare erosion predictions produced by the CAESAR-Lisflood LEM with field observations from the rehabilitated Pit 1 landform of the Ranger uranium mine, based in the Northern Territory, Australia (Figure 1). The ability to predict long-term erosion at a catchment scale is important, as the final rehabilitated Ranger landform is required to physically isolate buried tailings material for at least 10 000 years. CAESAR-Lisflood has been extensively tested and calibrated using data collected from erosion plots (0.09 ha) on the Ranger trial landform (TLF) for multiple years (Lowry et al, 2020; Saynor, Lowry and Boyden, 2019). However, until recently, it was impossible to compare model predictions with field observations for areas larger than the TLF erosion plots. We can now compare model predictions of Life of Mine Conference 2023 | Brisbane, Australia | 2–4 August 2023 116 gully development with field observations from the newly rehabilitated, 40 ha surface of Pit 1 over two contiguous one-year periods: 2020–2021 and 2021–2022. The aim of this study was to assess whether the CAESAR-Lisflood model can successfully predict gully erosion at a larger spatial scale.

Aug 2, 2023

By S Davidge, J Pile, R Schumann, E Kepe, W Stewart, M Ridd, B Rathi

Geographical constraints, including steep terrain and high rainfall, at the Ok Tedi copper-gold mine in PNG’s Western Province present Ok Tedi Mining Limited (OTML) with significant challenges in mine waste management. Waste management includes discharge of tailings into the local river system and storage of waste rock from the mine pit in waste rock dumps which erode into the river system. Copper bearing skarn mineralisation is associated with a massive sulfide (mainly pyrite) deposit. Consequently, waste rock and tailings can contain significant amounts of pyrite. Without appropriate management, these waste streams represent a risk of producing acid and metalliferous drainage (AMD) when discharged to the river system. Throughout the life of the project, OTML have developed evolving strategies to manage mine wastes and mitigate AMD.

Aug 2, 2023

By L Nicholls, A Parbhakar-Fox

Reprocessing Queensland’s historical copper tailings for critical minerals is an opportunity to circularise the state’s mining industry by converting waste liabilities into assets. Mine waste valorisation enables economic rehabilitation of legacy mining sites, improves environmental outcomes, and can establish a secondary resources sector that produces a sustainable supply of critical minerals to supplement growing global demands (Australian Government, 2023). The North-West Queensland Minerals Province (NWQMP) hosts numerous copper mines with known cobalt mineralisation (Degeling, 2020). Cobalt is a critical metal essential for green energy and battery technologies, however its global supply is at risk due to geopolitical and ethical concerns (Rachidi et al, 2021). Historically mining in the NWQMP has focused on copper, leaving cobalt to theoretically accumulate in the mine waste. First-pass geometallurgical characterisation of waste repositories by the Sustainable Minerals Institute identified Capricorn Copper as a key site for further investigation into cobalt deportment and recovery potential.

Aug 2, 2023

By G Simpson, S Dressler, A E. Levett

Tailings storage facilities often represent a mining operation’s most significant liability, posing serious environmental, social and safety risks. Almost all mining operations dispose of wet tailings (Valenta et al, 2023), which represents a risk of failure due to the potential for a rapid strength reduction of the unconsolidated materials leading to static liquefaction and subsequent dam wall failure. As a waste stream, tailings disposal, decommissioning and relinquishment aim to be inexpensive processes, ruling out many highly engineered solutions. Advantageously, tailings consolidation does not need to be immediate, opening the door for relatively passive, inexpensive consolidation methods that aim to gradually increase the strength of tailings over extended periods (ie years). In this paper, we present the prospect of novel in situ tailings stabilisation, learning from studies that have investigated the most erosion-resistant landforms on Earth.

Aug 2, 2023

By R Mackay, J Brereton

On 30 June 2020, the Mine Land Rehabilitation Authority (the Authority) superseded the Latrobe Valley Mine Rehabilitation Commissioner as the independent overseer of Victoria’s declared mine rehabilitation. Currently, only the Latrobe Valley’s three brown coalmines are declared, with each mine at a different stage of rehabilitation and closure planning: Hazelwood ceased mining in 2017, Yallourn is scheduled to end mining in 2028 and Loy Yang in 2035. The three declared mines are very large, close together (Figure 1) and present significant environmental risks that must be mitigated during rehabilitation and post closure. In addition to fire risks, the brown coalmines are inherently unstable and require continuous monitoring and management of ground movements and groundwater pressures. Since 2007 several damaging events have occurred, highlighting the difficulties of achieving long-term stability, even with good landform design and rigorous ground control management plans

Aug 2, 2023

By A Grant, H Thomas, C Clarke, B Fuller

Native seed and plants are often a core input into the rehabilitation process of mine closure. Native plant establishment rate and performance is a function of many input elements including seed quality (purity and germination percentage), processing, treatment, storage, supply chain handling, application and ongoing management. Traditionally native seed has been supplied with very limited supporting information which in some cases has led to establishment failures, repeated attempts at successful plantings, and biosecurity issues, leading to increased rehabilitation costs for mining companies. Product procurement scrutiny, and sustainability credential requirements, in all supply chains is increasing the need for transparent, accurate, verified, and certified information. This collectively continues to evolve towards a social license to operate in all industries, including mining. The Australian Seeds Authority (ASA), in partnership with native seed supplier AustraHort, traceability software company Trust Provenance and supporting partners including Transport for NSW and Queensland Transport and Main Roads (TMR), were recipients of a DAWE National Traceability Grant. The project team has developed a framework and traceability software platform currently being piloted within the Australian native seed industry. The framework and platform allow for all critical-tracking-events along the journey of a native seed batch to be easily captured utilising a smart phone application (APP) and the global standard for data capture. The platform provides a unique identification for each batch of seed and critical tracking event. The batch ID belongs to that batch and all supply chain stakeholders can contribute to batch traceability by providing more information/data to increase information integrity. This gives the end user, and other stakeholders, increased levels of trust in the product to support value, increase efficiency and reduce costs. For mining companies these efficiencies and cost reductions are in the form of information availability across site/company, potentially improved vegetation establishment, less rework and efficient monitoring. Ultimately this contributes to safe, stable and sustainable rehabilitation. By capturing data on harvest, logistics, storage (including temperature and humidity), aggregation/blending, treatment, application and ongoing management, the native seed industry and end utilisers of native seed have a greater level of product integrity, brand integrity and information integrity.

Aug 2, 2023

By J Butler, N Patel, S Narendranathan, S Rastogi

The Yallourn Mine (YM) commenced mining of brown coal in the 1920s under operation by the State Electricity Commission of Victoria (SECV), prior to it becoming privatised in 1996. The present owners of the YM, Energy Australia Yallourn (EAY), acquired the site in 2000 and intend to continue operations until scheduled closure in 2028. Brown coal extracted from the YM is fed into the Yallourn Power Station which supplies approximately 22 per cent of Victoria’s electricity. Water within the YM is collected and stored in the Township Field Fire Service Pond. Water levels within the Fire Service Pond (FSP) may fluctuate considerably depending on the requirement for storage at the site. With due consideration to the stability performance of mine batters adjoining the FSP, the authors performed slope stability assessments to evaluate the risk and consequence of batter instability during the potential pond filling as it approaches the ‘critical pool’. The critical pond is defined as the level at which the driving forces (destabilising forces) are at their greatest and the resisting forces (lake forces, friction) are at their lowest. This assessment was undertaken using the Mine Geotechnical Risk Index (MGRI) approach.

Aug 2, 2023

By S Johnston, L Howe, S Stevens

Regulators have an important role to play in fostering and enabling reliable, effective, environmental management. Given this, it is perhaps surprising that there has been relatively little debate on exactly how regulators can enhance their own approaches, in order to promote improved outcomes overall. The authors argue that applying research on High Reliability Organisations, as well as the University of Pennsylvania’s Penn Program Framework for Regulatory Excellence, to regulators has the potential to facilitate positive environmental results across the mine life cycle.

Aug 2, 2023

By C Tapia, B Hay

There is a close relationship between the mining industry, economic growth, and technological and social development. Mining activities benefit all economic agents, but only during a timespan determined by available reserves, primarily driven by internal factors such as production rate, technology and costs, and external factors such as prices determined by the market and taxes and royalties controlled by governments (Gordon and Tilton, 2008; Henckens, 2021; Tapia Cortez et al, 2018). The intense use of Mineral Commodities (MC) needed to maintain the current lifestyle has placed ore depletion as one of the main threats to sustainable economic, social and technological development. However, ore depletion does not only arise due to the finite nature of MC, price fluctuations or technological challenges. It is also triggered by the ‘economic limits of extraction’, determined by the optimisation of financial factors and technical restrictions within the regulatory framework (Henckens, 2021). Most reporting standards – if not all – define ore reserves as ‘the economically mineable part of a Measured and/or Indicated Mineral Resources’ determined by the technical and economic viability of extracting, processing and commercialising MC having the NPV as the fundamental decision – making tool (JORC, 2012; Krzemień et al, 2016). However, is the NVP an ‘economically’ basis index able to capture the complexity of economic phenomena? No, it is not, and it is crucial to define what is economic and financial. Although related, they are different disciplines. Economics studies the behaviour of the different market agents, including human behaviour and policies and explains the factors involved in the scarcity (or surplus) of goods and services. Finance focuses on financial systems and managing funds considering the time, cash available, and risk. It involves banks, loans, investments, and savings and can be considered a small subset of economics (Simon, 1959; Tapia Cortez et al, 2018). Given the economic nature of ore Reserves, endorsed by governments (Figure 1), NPV is an incomplete tool to determine ore Reserves as it limits the problem to simply maximising cash flows instead of optimising the economic benefits for all economic agents. In addition, using the rigid scheme of controlling taxation – unable to adapt to new market or economic conditions and systematically increasing – within the NPV framework only increases the sub-optimisation of ore reserves, mainly due to the systematic taxation rise. Short-term benefits of higher taxes and royalties are unlikely to offset the impacts of reducing exploration and project development in the long-term, which not only boosts ore depletion concerns but also reduces competitiveness, threatening economic and social welfare (Fisher, Cootner and Baily, 1972; Gordon, Bertram and Graedel, 2007; Lilford, 2017; Mardones, Silva and Martínez, 1985; Radetzki, 2009; Rodrik, 1982; Tan, 1987; World Bank, 2000). Holistic methods and collaborative tools, able to dynamically simulate and assess how changes in the technical settings, regulatory framework (taxes and royalties), and financial factors affect the availability of ore reserves and the impact for all economic agents, should be incorporated into the evaluation process to fill the gap.

Aug 2, 2023

By C Antonio, R Narayan, A Scheuermann, S Quintero, M Edraki, M Ziemski

Tailings are mostly loose material and can be classed as non-plastic sandy silts. They also tend to have low permeability, resulting in slow consolidation (settling) rates and low shear strength, even after consolidation. These qualities make them prone to liquefaction and, therefore, physical instability. In contrast, coarse waste rocks possess high shear strength and permeability when stacked in dumps. However, air and water flow-through the voids can cause acid mine drainage (AMD) with significant undesirable environmental implications. Combining the two waste streams before disposal by mixing presents a potential opportunity to harness the desirable properties of both waste streams. This practice is termed co-mingling and is prevalent within the coal processing industry. Co-mingling of metalliferous mine tailings is, however, a relatively new concept. By combining the coarse waste rocks and fine-grained tailings streams, the fine tailings tend to fill the voids between the waste rocks, reducing hydraulic conductivity and advective air (oxygen) flow into the rocks, thus possibly reducing acid mine drainage (AMD). Conversely, tailings are introduced to large frictional particles from waste rocks, providing reinforcement, stability, and possibly improved drainage. New separation methods are being introduced to the mining sector, which have the capacity to extract waste with significantly larger particle sizes. High Voltage Pulse (HVP) is a technology in which relatively coarse particles ranging in size from 30 to 100 mm are exposed to an electric pulse discharge. This pulse preferentially fractures mineralised particles while leaving unmineralised rock (gangue) intact. The stream can then be passed via a screen, with smaller mineralised particles reporting to the undersize and removing coarser unbroken barren particles, thereby eliminating waste. Coarse particle flotation (CPF) is another emerging new technology in metalliferous mining that applies flotation separation to coarser particles than conventional flotation. These new technologies produce different tailings (waste) streams with varying physical characteristics and possibly surface chemistries. This research aims to test new tailings mixtures by applying the co-mingling concept to blend the reject streams generated by these two technologies (HVP and CPF) with the tailings produced by conventional flotation. Combining different waste streams before disposal can improve stability and potentially reduce the acid mine drainage (AMD) rate, thus enabling early access to the tailings storage facility for rehabilitation and closure.

Aug 2, 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

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