Engineering & Mining Journal

FEB 2019

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Page 32 of 75

EXPLORATION FEBRUARY 2019 • E&MJ 31 companies are forming agreements and alliances with universities and research organizations with an eye toward expand- ing and/or refining exploration techniques and knowledge. As an example, a new four-year, $3.6 million research partnership between the University of Western Australia (UWA) and Rio Tinto Iron Ore will lead to improved ef- ficiency in geological modelling, through innovative data science solutions. Building on a collaboration that start- ed in 2010, a new partnership called Data Fusion Projects involves UWA's ge- odata algorithms team working with a Rio Tinto Iron Ore team led by Resource Eval- uation Manager Thomas Green. Green said the challenge faced by Rio Tinto's resource evaluation group was to quickly and consistently interpret and integrate increasing volumes of different types of data collected. "We were actively seeking automated solutions not to re- place but to assist our interpretation to model geology and resource, which can be inconsistent and uncertain," he said. The partnership involves diverse themes including the development of machine-learning-based methods and tools to integrate diverse drill hole data to model stratigraphy, their material com- positions and geomechanical proxies for resource evaluation and mining. The research also aims to incorporate advanced machine learning methods to improve certainty in modelling the spatial extent of subsurface geological interfaces. The team will develop image analysis and visualization methods and tools to assist the interpretation of large volumes of 2D and 3D data from satellites and drones for planning and geological mapping. UWA and Rio Tinto's past collabora- tion resulted in UWA's commercialization of automated downhole image analysis software in 2015, and a RTIO-driven joint patent application in 2017 on Automated Validation Assistant (AVA) for geological and mineralogical composition logging from rock samples from drill holes using machine learning. In North America, Vancouver Island University (VIU) recently announced a mapping-related research project fo- cused on the glacial landscape of Can- ada's North regions. The project, funded by Natural Resources Canada's Earth Sciences Sector, is aimed at assisting in the development of better remote pre- dictive mapping (RPM) methods for min- eral exploration. "Traditional methods of surficial map- ping, employing aerial photographs and field verification, are both time-consum- ing and expensive," said Brad Maguire, a professor in VIU's Geography department. He and Professor Jerome Lesemann of the school's Earth Sciences department are overseeing the project. The research project aims to develop a methodology for computerized detection of the sediment components of eskers — ridges of gravel and sand, which occur in formerly glaciated regions of northern Canada, and which can host diamond de- posits and other valuable minerals. Currently, RPM is a promising avenue of semiautomated mapping using widely available digital datasets like multispec- tral satellite imagery. "However, there are gaps in the methodology," said Lese- mann. "Part of the problem is that the type of imagery used to date gives us information about spectral characteris- tics of the surface, which reflects mostly the type of material on the surface, like bedrock or sand and gravel. The imagery does not contain information about the three-dimensional shapes of landforms." The VIU project team proposes to de- velop an esker element detection meth- odology based on deep machine learning supported by a Convoluted Neural Net- work (CNN). CNN uses computer algo- rithms to try and replicate complex cog- nitive processes of the human brain. "We will be using CNN to identify eskers from newly available, high-resolution digital elevation models (DEM) of the Canadi- an Arctic," Maguire said. Lesemann ex- plained the aim is to train a computer to recognize patterns. "The form and struc- ture of eskers are complex and if we can teach a computer to learn what an esker looks like, we may then be able to identify other eskers automatically," he added. Back in Australia, the newly estab- lished MinEx Cooperative Research Cen - tre (CRC) began operating last July at the University of South Australia and in Western Australia. Supported by a $50 million grant from the Australian govern- ment and more than $150 million in cash and in-kind support from industry partici- pants, the MinEx CRC is tasked to devel- op cost-effective and eco-friendly mining technologies related to in-field sensing and real-time data analytics. David Giles, chairman of Minerals and Resources Engineering at the Future In- dustries Institute, University of South Australia, and chief scientific officer for the CRC, said the objective is to enhance the efficiency of minerals exploration nationally. "In the Australian context, the cost of exploration for new deposits has risen over the past 30 years and our success rate has declined," Giles said, noting that cheaper and more effective drilling technologies have the potential to improve the discovery and affordability of identifying new mineral deposits. MinEx CRC is tasked to implement the National Drilling Initiative (NDI), a collaboration of government geological surveys, researchers and industry that will undertake drilling in underexplored areas of potential mineral wealth. Another part of of MinEx CRC's focus is to extend the capability of technologies such as Coiled Tubing (CT) drilling so it can drill deeper, is steerable and delivers the highest quality sampling. CT technol- ogy for deep rock exploration, developed by Deep Exploration Technologies CRC, holds promise of drilling at 20% of the cost of conventional diamond drilling and has been tested at a Barrick Gold site in Nevada, USA. In Europe, a consortium of research in- stitutions and commercial interests have joined in the X Mine project to look for increases in exploration efficiency by de- veloping equipment for scanning drill core samples on site using new, highly sensi- tive layered imaging technology based on X-ray fluorescence, as well as composition analyses. The consortium conducted rock classification trials in late 2018 compar- ing the mineralogical results obtained from Orexplore's XRF detector, Advacam's XRT detector and Antmicro's 3D camera. The X Mine project, with funding through Horizon2020, is an EU research and development program funded with 80 billion to award to European research ini - tiatives over a seven-year period (between 2014 and 2020). It is based on interna- tional cooperation between research insti- tutions from Finland, Sweden and Roma- nia, sensor and equipment manufacturers from Finland, Poland, the Czech Republic and Sweden, and end users such as min- ing companies in Bulgaria, Greece, Cy- prus and Sweden, along with Australian drilling services and equipment supplier Swick Mining Services.

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