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By S M. W Rajaratnam, P Varma, T L. Sletten

Mining employees undertaking shift work are at considerable risk of experiencing sleep disturbances and circadian rhythm disruptions. In the complex, high-risk environment of mining, these sleep and circadian disruptions lead to adverse impacts on employee health, alertness and safety. Evidence-based tools that effectively target improvements in sleep health amongst shift workers are needed. Variability in work hours and individual differences in the response to shift work mean that a ‘one-size-fits-all’ solution is often insufficient in complex work environments. Currently no interventions provide tailored support for shift workers based on their rosters, personal commitments and sleep habits. Existing technologies, such as smartphone applications and watches, allow sleep monitoring, but their strategies are targeted towards night sleepers, which does not translate to shift work contexts. To address this, our team has developed an innovative digital tool that implements biomathematical models to provide automated, personalised sleep and circadian intervention strategies to optimise sleep, alertness and performance during shift work. The tool uses mathematical models of sleep and alertness regulation that provide physiologically optimal times for an individual to sleep when experiencing circadian disruption. This program of work included co-designed development of a world-first app with shift workers and the examination of the preliminary efficacy of the intervention for improving sleep, alertness and performance.

Apr 16, 2024

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 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 C Sylvestre

Leaders have invested heavily in traditional safety management to improve safety performance over the years. This primarily involved: • fixing the environment (eliminating the hazard) • improving the system (more/less rules, procedures etc) • making safety more conscious (more/better interactions about the importance of safety). These approaches have helped improve safety performance significantly during the last 50 years and will continue to play a role in the future. Although these approaches may be part of an overall solution, they are not enough to prevent many incidents. Today, these three approaches are used more than before, and are used better than ever before. Yet, workplace fatalities in Australia have stagnated from 2013 to 2019, as have the number of incidents, even serious ones. In the USA, workplace fatalities have increased by 15 per cent in the same period.

May 5, 2022

By C Sylvestre

For the last 50 years, traditional safety management has improved safety performance by: • Fixing the environment (eliminating the hazard). • Improving the system (more/less rules, procedures etc). • Making safety more conscious (more/better interactions about the importance of safety). Although these approaches certainly have merit, and may be part of an overall solution, they are not enough to prevent many modern-day incidents. Today, these three approaches are used more than before, and are used better than ever before. Yet, workplace fatalities in Australia have stagnated from 2013 to 2019, as have the number of incidents, even serious ones. In the USA, workplace fatalities have increased by 15 per cent in the same period.

May 5, 2022

By M J. Davies, R S. Davies

Despite increased expenditure, the global discovery rate of world-class ore deposits has been in decline for over a decade (Schodde, 2017). Mineral exploration has traditionally followed an empirical approach, searching for evidence of mineralisation upon the surface of the planet, then drilling to define the extent of an ore body beneath the surface. As outcropping deposits in well-defined areas are depleted, there is a need for exploration to extend to less well-explored search spaces. One popular new frontier is exploration at greater depths beneath the Earth's surface. However, exploring for deep deposits with limited surficial evidence requires the implementation of predictive targeting methodologies (Hronsky, 2011; McCuaig & Hronsky, 2014). The Mineral Systems Concept (Hronsky & Groves, 2008; McCuaig et al., 2010) provides a framework for predictive exploration targeting. Ore deposit formation is recognised to be a focused mineralising event. These events represent self-organising critical systems and are underpinned by the interaction of complex, non-linear processes. Through the collection and analysis of datasets that map mineralising processes, it may be possible to predict the size and location of resultant ore bodies. CITATION: Davies, M J and Davies, R S, 2018. Beyond below-ground geological complexity: Developing adaptive expertise in exploration decision-making, in Proceedings Complex Orebodies Conference 2018, pp 95–98 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 21, 2018

By P Scholtz

Sustainability is one of those fuzzy words like innovation: everybody has an idea what it means, but if one starts asking for concise definitions then it becomes clear that everybody puts their own nuance on the meaning of these words. It’s not because these are difficult words per se, but rather because our understanding of them is highly dependent on the environment we work, live and play in. To remove any ambiguity and aligned with the risks that are faced by the human population today, the definition for sustainability that will apply for this paper is: generating value in the short term without destroying value in the long term.CITATION:Scholtz, P, 2018. Sustainable post-closure development – is it achievable?, in Proceedings Life-of-Mine 2018, pp 58–59 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 25, 2018

By A Reid, A W. Musk, O Jones

In this paper, the authors attempt to bridge the gap that currently exists between the duty-of-care responsibilities of underground mining industry personnel, and the multifaceted health challenges facing mineworkers in Western Australia’s metal mines where diesel-powered mobile equipment predominate. In addressing this issue, the complexities of the nature and extent of the challenges are discussed along with the current inadequacy of existing regulatory metrics. Support is given to the need for well-organised in-mine baseline studies to clearly define the distribution and aging characteristics of the toxic and carcinogenic pollutants within the mine ventilation system, with special emphasis given to exploring the validity of biomonitoring systems. The paper concludes with the proposal that a template risk assessment data sheet be prepared for the industry to provide mining personnel with a sound basic knowledge of the health risks involved and the control measures and precautions available for minimising and/or eliminating the hazard.CITATION:Jones, O, Musk, A W, Reid, A and Davis, C, 2017. Risk assessment tool for mines using diesel-powered mobile equipment, in Proceedings 13th AusIMM Underground Operators’ Conference 2017, pp 151–154 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Oct 16, 2017

By C Hawtin, B Miller, D Ashton

Ernest Henry mine (EHM) is an Iron oxide copper-gold sublevel cave mine located 30 minutes north of Cloncurry in North-West Queensland. The underground mine lies beneath open pit excavations, which were in operation from 1999 to 2011. Fault structures carry large quantities of water from the cover sequence aquifer into the underground mine area. The open cut excavations exacerbates vulnerability of the underground mine to the high intensity wet season storms of northern Queensland. These factors combine to make hydrogeological issues high priority for mine operation. Water-related risks include the cost of removing water from the mine, flooding risk and mud rush potential.Turnover of key technical staff in late 2015 at EHM resulted in the mine being left in an untenable position of losing the primary source of technical knowledge. Due to encouragement of cross-disciplinary activity coupled with workload and resource sharing, the management team were confident in utilising on-site personnel to offset loss of experienced personnel.Remaining geology personnel took additional responsibility for hydrogeological monitoring equipment and planning of upcoming work. Consultant input was limited to review only and an emphasis was placed on knowledge transfer to the geology team. Using this strategy, a 13 hole dewatering program was successfully implemented, achieving a significant water level drop in areas of elevated hydrological risk, whilst also upgrading the hydrological monitoring sensor array.Through a development program centred on cross-disciplinary exposure, the technical team was able to retain key technical knowledge despite staff turnover and produce strong results outside traditional training and roles. This paper serves as a case study on the methods, strengths and drawbacks of putting a focus on cross-disciplinary exposure as a method to combat staff turnover.CITATION:Ashton, D, Miller, B and Hawtin, C, 2017. Go with the flow – managing hydrogeological staff turnover at Ernest Henry mine, in Proceedings Tenth International Mining Geology Conference 2017, pp 129–136 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Sep 20, 2017

By J D. Pease, J M. I Tuppurainen, P D. Munro

This paper examines how legislation and regulation can be external drivers of technical change in mineral processing and extractive metallurgy and how the industry can respond. Two examples are considered; the production of copper metal from concentrates and the cyanide process for gold production.The Clean Air Act (1970) in the USA initiated a wave of attempted innovations in the extractive metallurgy of processing copper sulfide concentrates to meet forthcoming standards on sulfur dioxide emissions. Interestingly, a satisfactory technical solution existed (flash smelting and gas capture) but had not been widely adopted.Nearly fifty years later the primary copper industry finds itself with new challenges. One major challenge is how to deal with the increasing level of arsenic in copper concentrates. Most countries impose an upper limit on arsenic in imported concentrate; typically, 0.5% As or less. Higher arsenic concentrates can be blended with cleaner ones to meet the limit, but this tactic cannot be used indefinitely as the average arsenic level of global concentrates rises inexorably. Further, smelters (often in urban locations) cannot be expected to deal with both ever-increasing arsenic in the feed and stricter restrictions on emissions and disposal. A rising “inventory” of primary copper deposits are already constrained from development by high arsenic concentrate. These deposits will need to be developed if future copper demand is to be met. An economic and technical solution to arsenic is needed. The gold industry may also face challenges in the use of cyanide, by far the dominant lixiviant used in gold and silver production. Public perception has driven restrictions on the use or transport of cyanide in some jurisdictions. History suggests that such pressure is only likely to increase. While there are several alternative lixiviants, none can yet match the utility and economics of cyanide.Such important issues will not be resolved by incremental change or new equipment items. They require development of new process technology - a tectonic event rather than an incremental change. It requires sustained effort (and expenditure) over decades and is very unlikely to result from “business as usual” for producers, suppliers or academics. Past major process developments were driven by a dire need for economic survival or by legislative change. What will drive the future changes?CITATION:Munro, P D, Pease, J D and Tuppurainen, J M I, 2017. Technology change by regulation – a cresting wave?, in Proceedings MetPlant 2017, pp 45–58 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Sep 11, 2017

By M Andrew, H Reynolds, R Mills

As the global mining industry continues to grapple with ‘black swans’ (high consequence / low frequency events) for health, safety and environmental losses, the focus is shifting from risk assessment of these events to risk management using selected purposeful controls that reduce the risk.Fundamental to critical control management (CCM) is a soundly based rationale for selecting the right controls for each and every risk. The approach should be based on a thorough understanding of the dynamics of the event: What causes it? How frequently will it occur? How likely is the consequence to be realised? This is typically done through a bowtie and methodical numerical risk analysis. Core to this step, then, is the structure and content of the bowtie; specifically, the analytic architecture that is used to construct the bowtie. Different architectures yield different constructions, and bowties are no different.Although bowties for operational health and safety (OHS) hazards appear to have a common architecture, they are not generic. It is important to stress that they contain site specific information, are prepared by site based operational teams, and vary from site to site to reflect each site’s operational conditions. They are often a product of the knowledge and understanding of the operational teams involved, may be prone to human error, and potentially miss latent hazards that may have been embedded early in the planning, design or construction phase of a mine or facility, or within the mining equipment. Overcoming these deficiencies through constant re-evaluation of the risks, and challenging the content on the bowtie will help to minimise the chance that something will be missed.This paper will cover key themes in bowtie architecture, introduce the concept of barrier models for high consequence event (HCE) bowties and present a relevant case study to illustrate the issues that determine quality in bowtie construction and critical control (barrier) selection and management.CITATION:Mills, R, Andrew, M and Reynolds, H, 2016. Can we stop a black swan breeding? Advances in bowties and critical control management, in Proceedings International Mine Management Conference, pp 105-114 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jun 22, 2016

By G I. Lumley

‘Every mine is a pilot’ is an attitude which encourages the mining paradox of optimistic mine plans and equipment under performance; propped up by an environment lacking accountability. The premise that ‘every mine is a pilot’, appears to underlie the development and operating approach to most mines. It is the hypothesis of this paper that many mining companies, development teams and their managers largely ignore what is done elsewhere. Anecdotally, most planners don’t know whether the selected rates are high or low (rates based on theoretical models tend to be high – certainly higher than what most companies can achieve). Mines then consistently operate the equipment at rates well below targets; often more than 20 per cent below. Further to this paradox, mine performance data demonstrates the average mine is currently operating their equipment at levels of output not much more than half the output of those in the top ten per cent. On the whole mines don’t know the potential of their equipment and therefore don’t put appropriate plans in place to do better. After all, when ‘every mine is a pilot’ there is little reason to look to one’s peers for an indication of potential. This paper will outline why every mine is not a pilot and how mines do value some knowledge, but not as much as they should. This paper proposes a two-pronged approach to address this issue in the area of equipment performance. This approach is to:1. engage in better forecasting of equipment rates and better subsequent performance through benchmarking against industry standards2. provide conditions for more accountability.These parameters are linked and proper implementation of them will address the losses which many shareholders and financiers have incurred when investing in companies with unrealistic mining plans.CITATION:Lumley, G I, 2016. Is every mine a pilot? In Proceedings International Mine Management Conference, pp 75-88 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jun 22, 2016

"Geometallurgy is a discipline that seeks to improve the sustainability of the extraction of metals and minerals by promoting the smarter use of non-renewable resources, better energy efficiency and greater use of renewable energy. To achieve these goals, development of innovative technologies and approaches are being developed along the entire commodity value chain, starting with exploration and extraction and extending to reuse and recycling. This paper discusses the geometallurgy of industrial minerals with special emphasis on kaolin, a versatile industrial mineral applied in ceramics, paper, paint, plastics, rubber, pharmaceuticals, cosmetics, ink, sealants, adhesives, sanitary ware, glass fibre, toothpaste, animal feed etc. Aspects of kaolin extraction in south-west UK are analysed from a geometallurgical perspective. While geometallurgy already features in kaolin extraction, its role can be significantly expanded by combining advanced in-pit characterisation and orebody modelling with integrated planning and control of the refining and calcination process. This paper highlights the importance of innovation in realising the full potential of geometallurgy.CITATION:Glass, H J, 2016. Geometallurgy – Driving Innovation in the Mining Value Chain, in Proceedings The Third AusIMM International Geometallurgy Conference (GeoMet) 2016, pp 21–28 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Jun 15, 2016

By R Long

The founder of social psychology is sometimes identified as Kurt Lewin. In a 1947 article, Kurt Lewin coined the term ‘group dynamics’. He described this notion as the way that groups and individuals act and react to changing circumstances. Lewin theorised that when a group is established it becomes a unified system with unique dynamics that cannot be understood by evaluating members individually. The discipline of social psychology really emerged out of World War II and tackled questions associated with human judgement and decision-making by the Nazis. For a comprehensive look at the development of social psychology look at Abelson, Frey and Gregg (2004).Social psychology is concerned with the way social arrangements affect human judgement and decision-making. What the author has done is apply the knowledge of this new discipline to an understanding of risk, safety and leadership. This paper will explain some of the developments in social psychology, such as the discovery of the bystander effect, Groupthink, bounded rationality, heuristics, risk homeostasis and the Milgram effect.The social psychology of risk tackles the way decisions are made about risk in their social context. Unless organisations understand that decision-making is socially conditioned, they are unlikely to be able to tackle the complexities of culture change or the development of risk maturity.The paper will offer a new understanding of why people do what they do, how risk makes sense and why a ‘dumb down’ approach to safety is dangerous. A model will also be presented that shows the pathway to risk maturity.CITATION:Long, R, 2016. The social psychology of risk, safety and leadership maturity, in Proceedings Seventh International Conference and Exhibition on Mass Mining (MassMin 2016), pp 31–42 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2016

By J Coombes

"In today’s challenging climate of reduced staffing, limited funding and uncertain markets, we are called on to operate more productively and ‘be more innovative’. As an industry, we are quick to identify our practice areas as a mark of our role in the machinery of the mine value chain. We proudly note our profession – ‘geologist’, ‘mining engineer’ or ‘accountant’ – and demarcate our responsibilities and even our physical space. We even subdivide our disciplines into sacred entities such as ‘logging geologist’, ‘resource geologist’ or ‘structural geologist’ to prevent any blurring as though somehow the confusion will cost valuable disciplinary ground. How can we function professionally to heed the call to be more productive and more innovative?The purpose of this paper is to underscore what is expected from project evaluation, examine the ways in which we work as an industry and explore alternatives used in other industries and environments.The paper begins with a summary of project evaluation expectations and the ways in which disciplines typically interconnect when conducting project evaluations. Next, the paper examines alternative work frameworks and making a distinction between multidisciplinary, interdisciplinary and transdisciplinary frameworks. In-practice examples of these frameworks in alternative industries and contexts provide evidence for application as well as an opportunity to examine their pros and cons.The paper closes with a call to re-examine working practices in the mining industry, with a view to enhancing productivity and heeding the call for greater innovation.CITATION:Coombes, J, 2016. Transcending the mining discipline divide – multidisciplinary, interdisciplinary or transdisciplinary?, in Proceedings Project Evaluation 2016, pp 2–10 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Mar 8, 2016

By C Tapia Cortez, C Sammut, S Saydam, J Coulton

‘Like every other commodity, the value of the metals is subject to variation’ (Ricardo, 1817). Mineral commodity price uncertainties have always been a significant issue in mining business due to the close relation between global economic conditions and commodity prices, which commonly generates significant and unexpected price fluctuations. Price forecasts have been driven by economic theory, which states that in the long-run market will be balanced and that prices tend to the marginal cost of production. However, this theory has been misunderstood in the mining industry and future prices through time, in real terms, are assumed to be unchanged. This is unrealistic due to regardless of the cycling nature of economy and the omission of uncertainties. Furthermore, using historical data in traditional price forecast approaches does not guarantee realistic predictions as past events will not necessarily be repeated at the same intervals and with the same intensity. Mining, finance and economic sectors have made significant research efforts to predict prices and control uncertainties through classic linear, Gaussian and probabilistic simulation approaches. However, by definition, uncertainties cannot be measured or accurately modelled. Uncertainties can be identified to measure and manage the associated risks; therefore, uncertainties in mining projects have been historically misinterpreted, erroneously measured and poorly controlled. It is widely known that supply and demand functions drive market prices. While technological development noticeably influences supply, demand is strongly driven by psychological factors, mainly related to customer preferences and lifestyle expectations. Thus, human behaviour is one, or if not, the most important variable to determine commodity prices in a competitive market. During the last decades, alternative methodologies have been used to study uncertainties in human, environmental and market behaviours. Chaos theory, fractals, artificial intelligence and econophysics have been widely and successfully used to investigate complex dynamic systems to address uncertainties over time in the fields of neurosciences, meteorology, aviation and market trading. The application of these techniques in the market suggests a more realistic approach to the business environment. Through the combination of the methodologies above a novel long-run price uncertainties simulation approach that emphasise the differences between risks and uncertainties is proposed in this paper. CITATION:Tapia Cortez, C, Saydam, S, Coulton, J and Sammut, C, 2015. Copper price uncertainties – chaos theory to manage risks in mining projects, in Proceedings Third International Future Mining Conference, pp 261–272 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 4, 2015

By J Tibbett, A Colebourn, B Hebblewhite, F Suorineni

The concept of Big Data refers to data sets that are so large, variable and/or complex that traditional data processing applications struggle to integrate the data for effective investigation, analysis and value generation. With advances in sensor technology and smarter equipment, the mining industry is experiencing Big Data, and in a different way to how the intelligence and marketing communities experience Big Data. Future mining will bring larger, more complicated and diverse data sets increasing the strain on traditional data integration and investigation techniques. Block caving is an important mining method for the extraction of massive, low-grade ore deposits due to its low operating cost and high production potential, given the right orebody geometry and ground conditions. This paper focuses on block caving due to its increasing popularity and importance in the future economic extraction of multiple commodities. The block cave mining system (BCMS) is a complex interrelation of factors, and due to the reliance on natural processes within the system, it is one of the least understood mining methods. Obtaining information on the factors within the BCMS generates large, complex data sets on various time scales. Each sensor network within the BCMS combines spatial and temporal information with tens to hundreds of recorded parameters. Data sets are quickly moving from multidimensional to n-dimensional. Optimisation of the BCMS is only possible through a holistic analysis approach, thus involving the integration of multiple, diverse, n-dimensional data sets (Big Data). This paper presents a current example BCMS Big Data situation involving 12 sources of information with differences in volume, data format and frequency of data generation. This suitability of virtual reality scientific visualisation (VRSV) as a solution to BCMS Big Data interpretation difficulties is then discussed. These 12 data sets can be effectively integrated using VRSV for intuitive and accelerated investigation of the BCMS. Potential future Big Data within the BCMS is presented involving 20 sources of information resulting from our increased need for monitoring and advancements in technology. Current methods of defining queries may not be suitable within these future complex, multidimensional data sets, and the potential of artificial intelligence algorithms within this system is discussed. These advancements in technology and machine learning lead to the requirement of live data acquisition and analysis. VRSV is identified as a suitable technology to satisfy the needs of real-time insight into the performance of these future caves for optimised management and reduced risk.CITATION:Tibbett, J, Suorineni, F, Hebblewhite, B and Colebourn, A, 2015. Virtual reality scientific visualisation – a solution for big data analysis of the block cave mining system, in Proceedings Third International Future Mining Conference, pp 109–116 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 4, 2015

By R C. Morris

Two major types of economic secondary iron ores occur in the banded iron formation (BIF)-rich Hamersley Province of the Pilbara, Western Australia:dominant BIF-hosted bedded iron deposits (BID)detrital ore deposits (DID sensu lato), which are mainly hosted in the three province-wide Cenozoic sequences that include coeval non-ore sediments and comprise:a. the major Miocene, fluvial channel iron deposits (CID)b. the lesser Eocene and Pliocene detrital iron deposits (DID sensu stricto).Striking differences exist between the massive CID resources and the much smaller underlying and overlying DID. CID are essentially riverine alluvial ooidal rocks with abundant small fossil wood fragments and variable iron peloids, but with only extremely rare recognisable lithic remnants. The original matrix is typically ramifying layered goethite. Eocene DID are mainly alluvial with minor pisoids, whereas Pliocene–Quaternary DID are dominantly coarse colluvial gravels, both derived from and largely retaining the original textures of BID, hard cap or variably ferruginised surface BIF. The colluvial DID matrix is typically ferroan-aluminous soil, resulting in canga where replaced by goethite, which may be dehydrated to hematite by exposure. The Cenozoic deposits occur in two dominant geomorphological environments: the southern Marra Mamba to Brockman Iron Formation strike valleys containing all three Cenozoic sequences and the much later northern Brockman Iron Formation plateau valleys that include only the Miocene and Pliocene sequences. The Cenozoic detritals formed in different climatic regimes, with an extended dry period forming a prominent province-wide dehydrated carapace on the Eocene DID. The Miocene ‘optimum’ followed with its thick scrub-covered deep regolith that produced the fossil wood-rich CID and was succeeded by the arid cool period of the Oakover limestone/calcrete. A major renewal of exposure and erosion in the Pliocene resulted in the extensive iron gravels of the Pliocene–Quaternary.CITATION:Morris, R C, 2015. The Cenozoic detrital iron deposits of the Hamersley Province of Western Australia – a tribute to Mal Kneeshaw, in Proceedings Iron Ore 2015, pp 319–326 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 13, 2015

By A L. Stewart, M Baatar

"An EXTENDED ABSTRACT is available for download. A full-length paper was not prepared for this presentation.The Middle Palaeozoic Gurvansaikhan Belt (Southern Mongolia) of the Central Asian Orogenic Belt is one of Central Asia’s most significant emerging porphyry copper provinces. The belt has become the focus of extensive exploration for porphyry copper-gold deposits over the last decade following the discovery of the giant Oyu Tolgoi porphyry copper-gold deposit. The Gurvansaikhan Belt is known to host porphyry deposits of Late Devonian (~370 Ma) and Early Carboniferous (~320 Ma) age. The characteristics of Early Carboniferous porphyry systems are poorly known compared to their older counterparts, despite their probable abundance and potential economic significance. Exploration within the Kharmagtai porphyry breccia complex has identified multiple cogenetic gold-copper-porphyry centres and late-stage mineralised magmatic-hydrothermal tourmaline breccias emplacement within a composite high-K calc-alkaline quartz monzodiorite intrusive complex. Early porphyry alteration and mineralisation at Kharmagtai are zoned around vertically attenuated early- and inter-mineral quartz monzodiorite dykes and stocks and comprises a complex sequence of calc-potassic, potassic and propylitic assemblages. Hematite dusting is a common alteration product that gives intrusions and the host rocks a distinctive pink-orange colour. Several of the systems have bornite-rich cores, where mineralisation is characterised by high gold grades (0.7 to >5 g/t Au) that zone outward to chalcopyrite-rich and then outer pyritic halos. Early stockwork mineralisation is overprinted by magmatic-hydrothermal tourmaline breccias. High-grade magmatic-hydrothermal tourmaline breccias form part of a major copper-gold mineralised breccia system with significant exploration potential. The Early Carboniferous porphyry copper-gold systems of the Gurvansaikhan Belt represent a substantial exploration challenge in an area of increasing exploration maturity. This paper summarises the characteristics of the Kharmagtai porphyry system that have helped to define the Early Carboniferous porphyry province of the Gurvansaikhan Belt and discusses implications for future exploration.CITATION:Stewart, A L and Baatar, M, 2015. The Kharmagtai porphyry breccia complex – characteristics of early Carboniferous porphyry mineralisation in the Gurvansaikhan Belt, Southern Mongolia, in Proceedings PACRIM 2015 Congress, pp 383–388 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Mar 18, 2015

By P F. Hodkiewicz

"Our understanding of mineral resources relies on three basic steps: data capture data interpretation model construction. Until recently, all three of these steps required significant time and manual effort to produce a single resource model for mine planning, typically taking more than a year to progress from drilling to the final block model. Over the past several years, BHP Billiton has been developing innovative workflows in rapid resource modelling projects to significantly reduce these time constraints and make the process more productive by replacing manual activities with automated ones. Rapid resource modelling is the process of combining recently developed off-the-shelf technology with internal technology developments to fundamentally change the way we collect, interpret, model and evaluate geoscience data. Initiatives include automated systems and standardised workflows to make it safer, faster and cheaper to continuously update multidiscipline resource models. Specifically, rapid resource modelling workflows combine: real-time analysis of drill holes and drill samples with downhole tools and hyperspectral scanning systemsautomated interpretation of drill hole intervals based on pattern-recognition algorithmscontinuous modelling workflows using implicit modelling software. This paper outlines technology developments and workflows that provide a more thorough understanding of mineral resources to support faster and more informed strategic, planning and production decisions.CITATION:Hodkiewicz, P F, 2014. Rapid resource modelling – a vision for faster and better mining decisions, in Proceedings Ninth International Mining Geology Conference 2014 , pp 183–188 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Aug 18, 2014