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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 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 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 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 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 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 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

By Yakup Cebeci, Leyla Kalender, Ceylan Yarluğkal Altınışık, Hikmet Sis

The abstract highlights the challenges in concentrating complex and non-sulfide lead-zinc ores and emphasizes the importance of froth flotation for their beneficiation. The study focuses on using process mineralogy to understand the flotation behavior of these ores from the Görgü region. The ore samples were characterized using various techniques, revealing oxidation and carbonation of primary minerals. The flotation results were interpreted in light of the mineralogical data, explaining the poor outcomes due to the complex nature of the ore, poor liberation, and lack of hydrophobicity.

Mar 22, 2022

By Jessica M. Wempen, William G. Pariseau, Dallan J. Coons, Michael K. McCarter, Erik C. Westman

The abstract discusses the importance of proper pillar sizing in underground mining for safety and efficiency. It highlights the use of theoretical models, geotechnical measurements, and numerical modeling in predicting pillar behavior. The research focuses on using seismic tomography to image stress changes in a yield pillar at a longwall mine as the longwall face approaches. The study aims to compare the stress distribution imaged using tomography with theoretical models, geotechnical measurements, and numerical models.

Mar 18, 2022

By J. R. R. Bowling, A. McFadden, M. Francoeur

The abstract discusses the increasing use of extensive duct-fan systems in underground hard-rock mine development. It highlights the challenges of designing these systems, particularly the risk of partial vacuum and the need for careful booster fan positioning. The paper explores factors influencing the design of these systems, including component limitations, operational wear margins, and power constraints. It also defines the concept of partial vacuum and its impact on ventilation ducts. The authors propose an algorithmic approach for specifying the location and blade pitch of in-line booster fans to prevent system failure.

Mar 17, 2022

By Jared Winkler, Jon A. Hummer, Aleksandar D. Bugarski, Shad Peters, Steven E. Mischler, Steve Cochrane, Shawn Vanderslice, Teresa Barone

The case study assessed submicron aerosols at a continuous miner (CM) section, diesel aerosol concentrations at the longwall (LW) section, and elemental carbon (EC) and total carbon (TC) exposures. Aerosols at CM sections were a mix of freshly generated and those from main drifts. Low ambient concentrations and personal exposures suggest effective control strategies, with further reductions possible by curbing emissions from high-emitting light-duty vehicles.

Mar 16, 2022

By Dongjie Zhang, Shuxin Liu, Jianduo Wang

Broken rock mass and unreasonable stope structure parameters can cause excessive ore loss and dilution in sublevel caving; this is the main problem faced by caving mining in recent years. This paper proposes a stope structure parameter optimization method suitable for steeply inclined medium-thick broken ore bodies and evaluates the reliability and effectiveness through case studies. To solve the problem of large ore loss and dilution in the high-end wall sublevel caving method of Xilinhot fluorite mine, it is necessary to select proper stope structure parameters. Based on the theory of random media, an end-wall ore drawing experiment was carried out, and the bulk flow parameters needed to optimization of stope structure parameters were determined. In the experiment, it was found the draw-out body of high-end wall ore drawing showed a fine top and a thick bottom; the granular media had good fluidity. The reasonable caving step is determined to be 1.2 m instead of the 1.6 m originally used. Through the optimization experiment of the mining route position along the vein, the study found that the best route position is different with the ore body inclination. When the inclination angle of the ore body was 90°, the reasonable route was located in the center of the ore body. When the inclination angle of the ore body was between 85° and 90°, the route moved from the center of the ore body to the footwall at a rate of 22.5%. When the inclination angle of the ore body was between 80° and 85°, the route moved to the footwall at a rate of 54.8%. When the inclination angle of the ore body is not greater than 80°, the route should be arranged on the boundary of the footwall. Field application results showed the dilution rate reduced by 7.66% and the recovery rate increased by 7.31%. Field test results verify the effectiveness and applicability of the optimized stope structure, which corroborates the effectiveness and applicability of the optimized stope structure.

Mar 10, 2022

By L. Zhou, L. Yuan, R. A. Thomas, D. Bahrami

The abstract emphasizes the importance of a calibrated ventilation network model for mine ventilation planning, optimization, and fire simulation. It highlights the impact of continuous airflow monitoring on improving model calibration. The paper discusses an experimental study at the Safety Research Coal Mine (SRCM) to understand the use of continuous airflow data for model improvement. It also addresses practical problems in calibration and tuning using this data, including analysis of air velocity sensor fluctuations and application of sensor location correction factors for accurate average air velocity.

Mar 7, 2022

By Yousef Ghorbani, Abolfazl Danesh, Fatemeh Kazemi, Ataallah Bahrami, Morteza Abdollahi

The abstract highlights the impact of flocculants used in the middle thickener of a copper-molybdenum plant on both process water quality and molybdenite floatability. The study investigates the effect of a starch-based flocculant (wheat starch) on the efficiency of the middle thickener and molybdenite flotation. The findings indicate that the presence of starch influences the settling behavior of particles with different shapes, and the dosage of starch affects both molybdenum recovery and the presence of copper minerals in the molybdenum concentrate.

Mar 7, 2022

By Brian Y. Lattimer, Kray D. Luxbacher, Jonathan L. Hodges, Manuel J. Barros-Daza

The abstract discusses the importance of predicting fire response time (the time before conditions become untenable for firefighters) in underground coal mines. It introduces a data-driven approach using artificial neural networks (ANNs) to predict response time and fire size based on measurable parameters like CO concentration, mine geometry, air velocity, and time since fire detection. The approach was tested using simulations from fire models (FDS and FSSIM), showing promising results for real-time predictions during mine fires.

Mar 6, 2022

By T. Doshi, S. Ennis

The interactions of mineralogy, chemistry and mechanics in an ore concentrator are highly complex, so it can be particularly hard to measure the impact of a specific plant change. This paper describes a method for modeling a baseline metal recovery so that the value of a major process improvement can be measured. This method only considers variables that cannot be optimized, such as mineralogy or equipment availability. Linear regression, random forests, extreme gradient boosting (XGB), and simple two-layer neural network models of process value were tested as candidate baseline model types to assess the impact on throughput and recovery in a copper concentrator. XGB performed the best in terms of accuracy for both throughput and recovery models. Using an XGB model of nonoptimizable process variables provides an accurate, dynamic baseline that can be compared with continuous production data to determine the value added by major concentrator improvements in real time.

Mar 2, 2022

By L. Schernikau, W. H. Smith

Oil, coal, and gas account for approximately 80% of global primary energy, but only a portion of total airborne CO2 eq (approx. 35% at GWP20 to 65% at GWP100), even though they account for 95% of total measured CO2 emissions. The benefits of these energy sources, as well as their related costs, are not all incorporated in current energy policy discussions. Global greenhouse gas policies must include documented changes in measured airborne CO2 eq to avoid spending large amounts of public funds on ineffective or sub-optimal policies. The authors examined airborne CO2, which is less than half of emitted CO2, as well as reported CH4 emissions and the global warming potential of CH4 as published by the IPCC for coal and natural gas. The surprising conclusion is that surfaced-mined coal appears ‘better for the climate’ than the average natural gas, and all coal appears beneficial over LNG. Therefore, current CO2-only reduction policies and CO2 taxes are leading to unintended consequences and the switch from coal to natural gas, especially LNG, will not have the desired impact of reducing predicted future global warming; in fact, quite the contrary. A large portion of anthropogenic global warming is attributed by the IPCC and IEA to CH4, but it must be noted that CH4 emissions from natural sources and from agriculture account for approximately 40% and 25% of annual global CH4 emissions respectively. Energy accounts for about 20% of documented CH4 emissions. CO2 contributes only approximately 35% of annual airborne anthropogenic GHG emissions after accounting for CH4, over a 20-year horizon. At a 100-year horizon, the contribution of CO2 increases to approximately 60%. Energy policies that do not consider all GHG emissions along the entire value chain will lead to undesired economic and environmental distortions. All carbon taxation and CO2 pricing schemes are incorrect and need to be revised. At IPCC’s GWP20 an approximately 2%1 higher loss of CH4 across the value chain prior to combustion of natural gas versus coal will lead to ‘climate parity’ of coal with natural gas. According to public data, natural gas value chains have high CH4 and undocumented CO2 losses. On a global average, using only IEA-documented CH4 data, natural gas emits approximately 15% more CO2 eq than surface-mined coal, over a 20-year horizon. This difference will increase as the use of shale gas and LNG expands. Investors should support all energy systems in a manner that avoids an energy crisis, including intermittent renewable energy systems where they make sense. If CO2 emissions need to be reduced, one of the most effective ways would be to install ultra-supercritical power plants with CCUS technology. However, the undisputed benefits of increased CO2 concentrations in the atmosphere because of its promotion of photosynthesis and plant growth effects (fertilization) need to be considered in energy policy decisions as well. The authors suggest that future research and development should concentrate on reducing net emissions from fossil fuel power plants and providing cost-effective and reliable new conventional power generation capacity, utilizing clean coal and clean natural gas technology.

Mar 2, 2022