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By M Watson, A V. Engelbrecht, E Murray-Hayden

Whereas mine reconciliation is a well-established month-end process for most modern mining operations, the resource sector has yet to endorse uniform reporting factors against which model accuracy and metal recovery can be formally assessed. Logically, any comprehensive reconciliation framework should facilitate comparison between period reserve, mine and mill and production figures. Historically, Parker’s F Series ratios (Parker, 2012) are often used in some form by mine operators, although they are rarely shared externally, as formal definitions have yet to be accepted. This paper presents a structural extension to the reconciliation process, wherein resource ratios (R Series factors) are used to quantify the long-term impact of the information effect, together with dilution and ore loss. A case study from the El Limón Guajes (ELG) Mine Complex in Mexico is used to demonstrate how an R Factor Series can be implemented to facilitate operation optimisation, over and above conventional F Series reconciliation treatments. This approach is applicable to any mining operation.

Mar 22, 2022

By N Anderson

The world-class Golden Mile gold deposit in the Eastern Goldfields of Western Australia is part of the Kalgoorlie Consolidated Gold Mines (KCGM) operation, which is wholly owned by Northern Star Resources Limited. Current mining operations across the Golden Mile is dominated by the large Fimiston Open Pit, which is over 4 km long, 1 km wide and 600 m deep. It is part of a larger mineralised system that stretches over 8 km and includes the Mt Charlotte Underground Mine, which is also operated by KCGM, as well as numerous historical open pit and underground mines. This mineralised system has been continuously mined for over 125 years and is set to continue well into the future. The Golden Mile deposit is in the Norseman Wiluna Greenstone Belt which is made up of a series of mafic volcanic and sedimentary rocks that have been structurally deformed and intruded by granitic rocks. There are two main styles of mineralisation across the Golden Mile. Firstly, the Fimiston style, which consists of shear hosted Golden Mile lodes with abundant sulfides and tellurides and secondly, the Charlotte style that comprises younger stockwork veins. Fimiston style mineralisation is the primary style of mineralisation within the Fimiston Open Pit. Historically, in excess of 3500 m of underground development and over 755 stopes were mined prior to the consolidation of the area and the commencement of Fimiston Open Pit operation. Old underground workings, known as voids, are either empty or backfilled and extend to more than 1.2 km below surface. Although they are a routine part of mining operations at KCGM, voids pose a significant challenge for the geology team in terms of planning and executing exploration and grade control drilling, geological modelling, grade and tonnage estimation, drill and blast design and execution, marking up ore block designs, mining, and reconciliation. This paper will discuss the various challenges faced by the geology team due to the presence of voids, solutions used to overcome some of these challenges and the results of these activities.

Mar 22, 2022

By N Pranoto, E Lawell, T Dean, M Grant

The Goonyella Riverside Mine is located 30 km north of Moranbah in Central Queensland, Australia. Recent mining activities have exposed the north–south striking Kenya and Zimbabwe thrust Faults. These faults vertically offset coal seams by up to 25 m and locally thicken coal packages through imbrication of coal seams. The presence of back-thrusts add to the geotechnical complexity of maintaining highwall stability. The Kenya and Zimbabwe Faults have provided multiple challenges to the mining operation, with an unplanned additional 240 kt of coal tonnes being mined from imbrication of coals seams or from the refinement to local scale geological models. These additional coal tonnes have been either identified during the mining of the coal seam or just prior to overburden removal above the coal seam. These faults can also negatively impacted highwall stability with highwall failures, lowering the overall slope angle for pit designs and increasing stand-off distances for hazardous highwall conditions. Unmanned Aerial Vehicles are used to scan exposed highwalls and enable geological mapping via high-quality 3D images. Measure while drill data from drill and blast rigs can provide local-scale intersections of the faults and coal seams. Mine Geologists utilise these and other data sets to create localised geological models refined position of the fault planes and coal seams. These models are utilised by relevant mine site teams to ensure the safe and efficient mining of coal around these faults.

Mar 22, 2022

By H S. Pescini

A material aim when feeding ore blends into a processing facility is to reduce the variability of the properties of the feed material and to maximise the output, or throughput. At the Mining Geology conference of 2019, we presented a paper on optimal blending of coking coal given geological variability. The paper introduced integer-programming techniques to both maximise value and minimise feed or product variability. We discussed an application where the geological uncertainty is characterised using a normal distribution and exact solutions are readily obtainable using second order conic constraints in the mathematical model. Geological uncertainty is not always normally distributed, many properties will be characterised as log normal or some other skewed distribution. For example, copper can be some form of lognormal and Fe in Iron ore has a left skewed distribution. In this paper, we discuss some distribution free techniques to optimise the blends of plant feed or output given a quantified geological uncertainty. We discuss the sample average approximation method and how to set-up and apply the method to an ore blending problem. Evaluating a case study demonstrates that the explicit use of non-normally distributed geological uncertainty can better optimise feed into an ore processing facility and potentially add significant value. The approach is equally applicable to all mining and processing operations that blend material into a processing facility or directly into a saleable product.

Mar 22, 2022

By R J. Goldie

The Monty Copper Gold Deposit is an ultra-high grade mine in Sandfire Resource’s Doolgunna region which was completely drilled out and developed within two years of commencing mine development. During this period, a total of 986 grade control diamond drill holes were completed, assayed, and validated, 15 levels developed, and 3136 faces mapped. The aggressive schedule meant a high flow of geological data was produced, validated, and incorporated into regular geological model updates. To process this data at a rate which allowed for timely use, more robust data processing methodologies needed to be generated. A new validation system was implemented which required two levels of validation being applied to any diamond drilling update. Step one was the validation of initial physical data including survey pick-ups and logging. This was done prior to new drill data being added to the geological model. Step two was the validation of assay data which was required prior to resource estimation. The implementation of Seequent’s Leapfrog Geo modelling software also allowed for a more seamless and efficient modelling workflow. The use of the Leapfrog Geo vein systems allowed for more rapid generation of geological models which then afforded more time for stringent assessment of the mineralisation system. An example of this was the introduction of cross-shift peer reviews in which opposing rosters of geologists would rigorously evaluate the previous rosters work prior to reporting. This additional level of validation resulted in more robust geological models and the identification of new ore trends. This presentation will detail the evolution of the Monty Geological model from a simple tabular indicated resource into a complex highly refined measured resource. Themes will include how the model was produced using workflows developed and optimised to meet key deliverables.

Mar 22, 2022

By M Akop, G Phillips, L Elliott

An estimation geologist has many tools in their kit and using the correct one for the job is vital for creating a useful model. Reconciliation is a key step to monitor the performance of a resource model and to ensure the correct tools have been selected and appropriate parameters are set. This paper presents an example of refining the estimation methodology for a Channel Iron Deposit (CID), resulting in an improved model. Rio Tinto Iron Ore (RTIO) CIDs are unique iron ore deposits located in the Pilbara region of Western Australia and contribute a significant volume to the world’s seaborne iron ore market. A problematic feature of these deposits is the irregular dispersion of clay pods that cannot be delineated with confidence within the resolution of the exploration drilling. Resource to grade control reconciliation showed an over-prediction of high-grade material in the resource model. A fresh approach was required to improve grade and tonnage predictions. Historically, clay pods were delineated using traditional wireframing techniques and estimated using ordinary kriging with hard boundaries. The new method utilised indicator kriging to estimate the probability of clay. Probabilities generated from the indicator kriging estimate were used as an additional weighting following the ordinary kriging of grades. Results showed indicator kriging improved global high-grade tonnes reconciliation, however, as expected, it did not improve the spatial prediction of clay. Based on improved reconciliation results indicator kriging has now been adopted as a suitable alternative for analogous CIDs at RTIO. These improvements benefit the mine planning process, but do not replace grade control models.

Mar 22, 2022

By W Patton, I Minniakhmetov, H Talebi, U Mueller

The sustainability of mining projects is linked to informed investment decisions based on public reporting of exploration and mineral resource estimation results. In Australia, public reporting guidelines are established by the Joint Ore Reserves Committee reporting code through the JORC Code (2012). Although the assessment of uncertainty in the results reported is a requirement, this is often communicated qualitatively and evaluated subjectively. This can become a liability particularly in the early stages of mining projects when spatial domains of geological interpretation and mineralisation envelopes inform resource estimations’ reliability. A recent review of JORC reports found no reports used quantitative assessments of the geological interpretation or mineral resource estimation envelopes, and 27 per cent of reports did not address the question of quality of geological interpretation. Reports that did address the quality used 19 different terms to communicate quality. This work presents methodologies for quantitative uncertainty assessment and communication and explores how they could be applied in public reporting practice. Quantitative uncertainty reporting would benefit mine planning and financial modelling as they normally assume the geology is 100 per cent right (since no error is reported or only a single deterministic model is provided). The complexity, cost, and additional work of doing a quantitative assessment could hinder a straightforward implementation. This could be overcome if mining companies budget for quantitative uncertainty assessment and associated professional development. A compulsory requirement for the inclusion of uncertainty assessments in public reporting or adopting standardised subjective language would improve industry practice.

Mar 22, 2022

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

Reconciliation is a powerful tool used by mining operations to compare the mass and grade of ore produced with what was estimated before mining. The primary objective of reconciliation is to verify that the estimated planned metal production is achieved during processing, allowing issues to be identified for which adjustments can be made throughout the mining value chain to improve recovery and maximise efficiency. Traditionally, the mine plan is based on estimated mine grades using sampled exploration drill hole or blasthole grades. Reconciliation discrepancies between the mine plan and mill are common due to such factors as insufficient or non-representative sampling, poorly conceived models, or unrecognised blast movement. MineSense Technologies Limited® (MTL) has developed an X-ray fluorescence (XRF) sensor-based ore classification system that is installed on mining equipment to provide real-time grade estimates of run-of-mine material at bucket, truck, or belt-scale. Bucket level grade estimates provide insights into grade variations at the mine face that were never before available. This data set eliminates some of the leading causes of reconciliation discrepancies and enables tracking of tonnages and grades from the mine face to the processing plant at bucket resolution with confidence. Bucket-by-bucket grades at the mine face enable dynamic optimisation of the mine plan to stabilise the mill feed grade through blending of mine and stockpile loads. Beyond stabilising feed grade, Mine to Mill reconciliation combined with the full XRF spectrum response can generate proxy relationships to ore characteristics like hardness and recovery. These properties can then be predicted at the mine face, enabling further optimisation of the downstream mill feed, not only for grade, but also for power and water consumption, reagent addition, and recovery. This paper presents the benefits of MTL technology for grade reconciliation and mill performance optimisation at a Cu Mine in BC (Canada).

Mar 22, 2022

By N Anderson

With traditional grade control processes, geological models are created by geologists at the completion of the data collection stage and wireframe construction is closed off at a set date to allow for time to complete the estimation process. Any issues identified at earlier stages or the addition of new data requires the estimation process to be started again. Alternative interpretations are explored only if time allows and often in a limited fashion. An alternative workflow has been developed at the Waihi Underground Gold Mine utilising Leapfrog Edge and Seequent Central. Geological and grade control models are created, dynamically linked, and saved as separate branches. A project geologist is able to work on the grade control model and a mine geologist on the geological model simultaneously. This allows for a non-linear modelling workflow with continual review and feedback. The introduction of new data can occur right up to the model due date, with data collection, validation, alternate interpretation, and review happening incrementally. Models are categorised and stored in Seequent Central according to project status. An approved version of the geological model and grade control estimate are uploaded at the time of release and transparently capture all data used at a point in time, as well as the review process. Dynamically updating models for grade control has reduced turnaround time while increasing accuracy and quality. Estimates of individual domains are vastly improved from being able to rapidly evaluate the effects of model parameter changes in real time and 3D. Development and stope design directly benefit from these streamlined processes, with higher quality, timely production occurring as a result.

Mar 22, 2022

By I Minniakhmetov, J Ashford

Orebodies constitute naturally occurring, commonly highly variable concentrations of minerals in the earth’s crust formed through varying and highly unpredictable processes. Orebody knowledge is a core foundation of a mining company’s asset base underpinning all decisions about Mineral Resource and Ore Reserves, future production, development and ultimately the profitability and competitiveness of a mining enterprise. The decision to invest in a mineral project is usually based upon a techno-economic evaluation of the project and having clear approval pathways to gain local community support and address social and environmental impacts. The main objective of the techno-economic evaluation is to determine the optimum development sequence for the particular orebody in question and to estimate the economic viability of the project and the future return on investment to its stakeholders, balancing the inherent value of the mineral in the ground against the costs of development and market pricing. With the exception of market-driven commodity prices, virtually all revenue and cost estimates fundamentally stem from the understanding and subsequent modelling of the orebody. A reliable estimate of the ore deposit grade and other geological value-drivers is the foundation upon which the business case for future mine development and operation is intricately dependent. Orebody knowledge is, therefore, the fundamental driver of the mineral project cycle, influencing the progression of studies and consequently, the resource company’s ability to declare Exploration Results, Mineral Resources or Ore Reserves which govern the source and cost of funding available to the project. Drilling provides a ‘vector’ to the true nature of the orebody and effectively buys time to manage the technical risk associated with the orebody. Despite its perceived high cost, in both time and money terms, drilling is the only available technology for acquiring the necessary orebody knowledge to support technical and economic studies and to minimise orebody-driven cash flow uncertainty and volatility. However, like all costs, pressure to minimise drilling budgets is ubiquitous across the minerals industry. Paired with inappropriate key performance indicators, drilling is often regarded as a cost to be minimised, rather than an investment to be optimised. Furthermore, as future deposits get deeper and more complex, the collection of orebody knowledge is increasingly on the critical path for project development. The increasing cost of data collection pertaining to these deposits increases the temptation to minimise orebody knowledge gathering at the cost of long-term value as the reduction or postponement of drilling results in greater orebody knowledge risk at all stages of the value chain. Indeed the true cost of the decision to drill less is rarely known until the mine is in operation, well after the mine project evaluation team has moved on. This paper aims to promote orebody knowledge as the fundamental source of endogenous project financial risk and, consequently, its critical importance in project financial evaluation. Furthermore, the report documents experimental research into the development of an econometric model for valuing orebody knowledge. Using the density of drilling data as a proxy measure of orebody knowledge, the proposed approach integrates well-established stochastic grade modelling techniques with the mathematical mechanics of Decoupled Net Present Value (DNPV) which, combined with stochastic grade modelling (conditional simulation), lends itself to the financial International Mining Geology Conference 2022 | Brisbane, Australia and Online | 22–23 March 2022 3 assessment of orebody knowledge risk. Based on this approach, a metric is formulated, representing the minimum value of the orebody knowledge added by additional drilling

Mar 22, 2022

By A D. Wulfse, C M. D Barton

Underground fractured aquifers in the Ernest Henry deposit are to a large extent controlled by deposit scale brittle structures. The underground mine has been subdivided into nine water zones based on hydrogeological characteristics, including conductivity and connectivity, within the mining areas bound by these structures. The nine water zones and the hydrological characteristics of the associated structures are utilised for risk assessing mine development and for targeting diamond drilling dewatering activities. Wireline and conventional underground diamond drilling techniques have been used throughout the network of underground aquifers with the aim of dewatering areas within the mining footprint. These dewatering drilling campaigns have historically been highly successful at lowering aquifer water levels throughout the underground workings. As part of recent mine expansion activities, diamond drilling has intersected high pressure (>507 psi), high temperature (>50°C) and high flow water (>3 L/sec) in as yet unconstrained underground aquifers. Groundwater is typically conducted through rubble and clay rich faults, further hampering progression of drilling. Drilling through fractured aquifers at depth presents both mine planning as well as safety challenges. Recent drill holes have been delayed or abandoned when aquifers hosting this high pressure, high flow water have been intersected, risking mine planning deadlines. Recent technological innovation developed to control groundwater outflows from the diamond drill hole whilst drilling has been trialled at the Ernest Henry Mine. This case study focuses on the utilisation of alternative drill methodologies to allow the current drilling campaign to continue without delay, whilst also improving safety conditions encountered on the drill site.

Mar 22, 2022

By K L. Silversides, A Chlingaryan, A Melkumyan

Multiple data sources can be fused to improve boundary modelling in stratified deposits such as the banded iron formation (BIF) hosted deposits in the Pilbara Region of Western Australia. Accurate boundaries are essential to produce high quality geological models. Initial models are based on the widely spaced exploration hole data. This creates a high level of uncertainty in areas between the holes, with different interpretations of the boundary equally probable. Including directional information collected in these holes in the models can reduce the number of possibilities. Including data collected during mining, such as blastholes and bench measurements, can further guide the model. This research demonstrates a Gaussian Processes based method of fusing these disparate data sources to better inform the boundaries. Our results demonstrate that when only exploration points are used the model creates a smooth boundary with minimal variation between the holes. Each additional data source refined the model and added additional detail to the boundary. For the blast data, the new detail projects down to the bench below the current bench to allow for a better prediction before mining commences on that bench.

Mar 22, 2022

By D Reid, J McKibben, M Noppe

International mineral disclosure standards and guidelines aligned with the CRIRSCO (Committee for Mineral Reserves International Reporting Standards) template were developed to inform stakeholders about the relative accuracy and confidence levels of publicly reported information and to support investment decisions, particularly when advancing projects through different development stages. The traditional stages range from early to advanced exploration, through pre-development (with Mineral Resources and pre-feasibility and/or feasibility levels of study) before the final development stages, where a decision to proceed with construction or production (or both) is made. The traditional definition of Mineral Resource and Ore/Mineral Reserve classification categories and techno-economic study levels (including Preliminary Economic Assessment (PEA), Scoping Study (SS), Pre-Feasibility Study (PFS) and Feasibility Study (FS)) were defined with pre-development project assessment in mind. As such, these definitions are not necessarily well aligned to the reporting structures required by operating mines which need to consider a range of timelines spanning short to long-term operating conditions. However, the same terminology and definitions are used, or expected to be used, by both project developers and mine operators when reporting resources, reserves and techno-economic study outcomes. This paper discusses how various stakeholders in the minerals industry (including competent/qualified persons, mining company leaders, investors, lenders and regulators), generally interpret the definitions of Mineral Resource and Ore Reserve categories in terms of the relative accuracy of the estimates, their link to techno-economic study levels and their contribution to life-of-mine (LOM) schedules. The paper examines the degree to which mining companies recognise, and apply, enhanced operational knowledge to reflect short-term views of the relative accuracy and confidence levels associated with publicly reported Mineral Resource and Ore Reserve estimates and forecasts. Furthermore, this paper explores how this knowledge is applied to support ongoing Mineral Resource, and particularly Ore Reserve, generation and reporting. This paper is informed by public data and responses to an internal survey prepared by the authors and completed in 2021 by several mining companies.

Mar 22, 2022

By F Faraj, M Pal, M Haest, M Bradshaw, H Lang, K Hume

MineSense Technologies Ltd. is revolutionising grade control at mining operations by installing ShovelSense® sensors on buckets that measure the grade of every load before it is dumped in the truck. This allows the mine to exploit grade variations at a resolution that was not available before, reducing the amount of ore loss to the waste stream and removing waste from material destined for processing. These changes dramatically improve the profitability and sustainability of mining operations. ShovelSense relies on X-ray fluorescence analysis of the rocks as they flow in the bucket and is integrated with fleet management to direct the trucks to the right destination. Lab simulations of ShovelSense measurements on porphyry copper ores show correlations between predictions and lab measurements at 0.98 r-squared with a coefficient of variation of 10 per cent. Visualisation of copper grade predictions in three dimensions shows a reasonable match with the dig plan, but with local changes in the ore-waste contact due to more accurate contact identification, related to blast movement and other mining factors. Correct delineation of these contacts ensures ore trucks are not sent to the waste dump and waste trucks are diverted from the crusher, creating enormous value for the operation and the environment.

Mar 22, 2022

By J Taylor, M Smith, A Tunnadine, D Nikolakopoulos

As Olympic Dam rapidly expands into the Southern Mine Area (SMA) the Mine Geology team has concurrently ramped up underground diamond drilling to sustain rates of above 175 km/a. With >10 underground rigs double-shifting, traditional paper sections used for the management of drill programs were no longer optimal due to scale of operations. An opportunity was identified to modernise monitoring of all underground diamond drill programs, through the integration of logged copper sulfide estimates with Olympic Dam’s first-fully integrated deposit-scale geological model in Leapfrog Geo. This paper describes the transition within the Mine Geology team from reliance on 2D paper sections, towards near-real time 3D geological modelling for optimising end-of-hole decisions in the field. This transition was enabled by innovative database solutions informing locally constrained implicit models, limited by a distance buffer around active underground fan drilling. Due to the high volume of historic drilling, there were significant computational challenges with the implementation of daily production style implicit models at Olympic Dam and initial testing of regular updates to implicit gradeshells and sulfide domains required in excess of 12 hrs to run. The enabling solution was to optimise the interfaces between the AcQuire database, Deswik MDM and Leapfrog Geo to create a dynamic, streamlined workflow that was easily completed by Mine Geologists in accordance with daily production duties. Geologists are now building rapid implicit models for a range of variables from an integrated data set of logged sulfide estimates and existing assays, spatially constrained by 150 m buffers around active drilling using a unique drill fan identifier flagged during the planning phase. Once the live-implicit models are updated, they are exported for visualisation on field tablets to be used during logging and core checks at the start and end of every shift. To manage the risk of ‘going digital’ interpretation remains at the forefront of the drill section close-out process, with Mine Geologists critically reviewing the finished sections on a standardised template with new learnings wireframed and communicated to stakeholders. The Live Field Model has enabled a step-change in the geological effectiveness of diamond drilling, reducing over – and under-drilling ore-waste at Olympic Dam continuing to push drilling effectiveness towards 20 per cent of intercepts >2 per cent Cu. In addition, upside or risk identified during the proactive management of drilling execution can be rapidly capitalised or mitigated.

Mar 22, 2022

By A Whaanga

The use of photogrammetry has greatly increased the accuracy of 3D mapping underground. However, this has not reduced the need or desire for geologists to produce plans and sections showing an interpretation of the mapped geology. At the Waihi Underground Gold Mine, a hybrid solution has been developed that utilises the accuracy of photogrammetry and mapping onto face photos with the CAD-based Deswik.MappingTM functions to create plan and section maps. These have the benefit of looking like traditional paper maps but are more spatially accurate and richly attributed with metadata that can be filtered and exported to downstream processes. Deswik.MappingTM is a tablet-based solution that allows the geologist to map digitally with a stylus. Automation is utilised for the updating of survey as-builts, approved designs, drill holes, geological mapping, vein interpretations and mine work areas, reducing the requirement to print out maps at various locations and scales. Having 3D mine software on a tablet device provides a wealth of features not available when using paper: scaling is a problem of the past; information can be turned on and off; features can be viewed in 3D; and measurements between points of interest can be calculated in 3D space, for example, between vein wireframes and drill hole sample information. A suite of custom scripts has been developed that take the spatially accurate, registered and attributed polyline data from photogrammetry scans and converts them to a geological mapping legend with vein textures, structural information, and lithological boundaries. These 3D maps can be plotted in plan or section view and satisfy the requirements of the most ardent paper map supporter.

Mar 22, 2022

By J Ashford, Minniakhmetov

Geostatistical simulations are the best practice for uncertainty quantification in non-linear systems. Simulations are currently undertaken on an ad hoc basis in the mining industry due to high requirements in computing environments and advanced geostatistics. In contrast to the estimation process, multivariate simulations can be performed using different methods, tools, and computational environments, similar to data science. For large companies with multiple commodities it is important to have a standard framework to test, build, and run workflows using different methods, applications and computational environments. This work presents an auditable, modular and scalable framework to create geostatistical simulation workflows where the workflow is abstracted from the implementation. Key aspects: automation – workflows should be able to run automatically given updated data (if hands on modelling is not required); auditability – workflows should document key considerations and parameters; modularisation – workflows should permit swapping out components, for example substituting domaining in Leapfrog software with automated domaining using machine learning scripts; scalability – easy, repeatable, portable deployments on diverse infrastructure (for example, experimenting on a laptop, then moving to an on premise cluster or to the Cloud); reproducibility – parameters should be archived to permit re-running and reproduction of workflows in future years; and sensitivity analysis – any component of a workflow should be able to run with multiple scenarios of parameters and data to enable global uncertainty quantification and sensitivity analysis. The presented framework can be used to create different workflows for geostatistical simulations in multiple assets to quantify uncertainty in the value chain for resource categorisation, reserves uncertainty, value of information and robust mine planning amongst others.

Mar 22, 2022

By B Haase, R Saha

Conditional simulation (CS) has been extensively used in metal industry to assess uncertainty of geo metallurgical variables to build robust distributions around grade tonnages across different time horizons. Coal’s entry to nonlinear stochastic modelling is relatively new and this paper summarises the work done at BHP to enable its practical usage from production time frames to Life of Asset planning. CS in coal using Sequential Gaussian Simulation (SGS) is used to understand resource confidence around specific variables within pre-defined boundaries of spatially continuous domains for each coal seam. The variation between simulations for a given parameter reflects uncertainty. This paper demonstrates the use of CS to profile resource based upon agreed seam/parameter uncertainty thresholds which affect planning, forecasting, extraction and inform identification of key production risks and defining mitigation strategies. The end goal is to de-risk long-term plans by optimising infill data collection and improve stability in short-term planning through increased understanding of confidences locally rather than at global resource definition stages. The paper also briefly discusses the use of automated workflows and systems to minimise the time spent on the process to enable use in decision-making.

Mar 22, 2022

By S Sullivan

A recent review of resource reports lodged to statutory bodies found geological models with oversmoothed or unrealistic lithological boundaries, many of which do not represent geological appearance in the field, pit or underground drive. The underlying data is often too complex or messy to be incorporated into the resource model and is thus a simplification of reality, to the detriment of the mining operation and its shareholders. Volumes can be over- or underestimated and subsequent grade predictions can be misplaced, combining to cause over- or understatement in resource reporting to stakeholders and statutory authorities. Domain models can be improved with new modelling techniques such as machine learning. However, with poor data or improper use of machine learning techniques, the conclusions can be just as misleading as with current techniques. Machine learning rarely offers a single ‘right’ outcome, rather a range of possible outcomes from the data provided. But importantly it can take advantage of the full richness of data for more informed models. Machine learning also provides confidence in its predictions so that uncertainty in the resultant models can be quantified and passed through for risk assessment during mine planning and operation. This paper will discuss the factors accentuating complexity in deposit modelling: data diversity, structural controls, chemistry, data volumes, process workflows and external non-geological constraints. A case study illustrates the risk of reducing or ignoring complexity, which can result in an overly simplified geological model.

Mar 22, 2022

By E J. Hill, F Huang, A Otto

Geochemical data from drill core samples is used in mineral exploration to enable geologists to understand subsurface geology. However, it is challenging to analyse these data sets rigorously and consistently. Manual data interpretation relies on the knowledge and experience of logging geologists. When multiple geologists are involved, it is possible to introduce inconsistencies in logged lithology type and scale of logging (‘splitters’ versus ‘lumpers’). Machine learning (ML) is a powerful tool for analysing high dimensional data (ie data with many variables) and large data sets (ie data with many samples). However, ML methods applied to drill hole samples do not incorporate spatial information and this can result in high misclassification rates and small-scale ‘noisy’ results. We demonstrate the use of multiscale spatial analysis (wavelet tessellation) as a pre-ML step to analyse spatial information. This step helps reduce misclassification and filter out unwanted small-scale variation in the data. In an experiment, we used wavelet tessellation and supervised ML to predict litho-geochemical rock types from geochemistry data. The data set is from the Valhalla uranium deposit near Mount Isa, northern Queensland. First, geochemical knowledge and statistical analysis was used to select appropriate geochemical elements for litho-geochemical classification. Second, the elements were processed using multiscale spatial analysis. Third, we trained a Random Forest model with the data set and found that the model performs worse in predicting rare rock samples than common ones. We applied over-sampling and down-sampling methods in aim to overcome this issue. As a conclusion, the combination of multiscale spatial analysis (to define rock unit boundaries) and Random Forests (to classify rock units) is a good method for rapid and accurate analysis of drill hole geochemistry.

Mar 22, 2022