Engineering & Mining Journal

FEB 2019

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RESOURCE MODELLING FEBRUARY 2019 • E&MJ 39 www.e-mj.com generated from a geological database within a half hour. Such speed is made possible by a deep-learning process that models multi-variable and multi-domain data simultaneously, Maptek reported. "The 30-minute scenario is indicative of the gains in speed that can be made," Sullivan said. "There's no hard and fast rule, since connection to cloud process- ing services allows users of the solution to run as many computers as are avail- able for processing." One effect could be cost savings, he said. "Reduction in costs results from the speed of processing, allowing fewer peo- ple to work more efficiently." Ease-of-use could further add to those savings, Sullivan said. "Deep learning for grade control embeds the resource ge- ology and modelling workflow inside the system ensuring consistent outcomes," he said. "The mine manager no longer is at the mercy of having the right skills, as the skills are provided through use of the Maptek machine learning system." Other benefits include increased au- tomation optionality in grade modelling and, in turn, mining, Sullivan said. "Data can be collected from autonomous drill rigs with onboard analytical capability and fed direct into the machine learning engine for grade estimation, followed by automated grade control optimization and then uploaded into fleet management dig control systems for mining," he said. Maptek reported the system is optimal for companies with projects with close-spaced resource drilling and grade control data. It is also ideal for miners who are "reviewing potential acquisitions or investment into new mining projects, whether internal or external," Sullivan said. "Often the technical review team has limited time to review each project and make their investment recommendation to management. Deep learning compresses the evaluation time, allowing more time for analysis prior to decision-making." Any miner, however, with sufficient data for a prefeasibility study could ben- efit from at least "an assessment of their project for its potential to benefit from machine learning," Sullivan said. Adoption and deployment of the soft- ware requires no prior relationship with Maptek, Sullivan said. "The only depen- dency is having data ready to upload into the system," he said. "Deep learning for grade estimation is available to the en- tire market," Sullivan said. "Data upload formats are generic, and outcomes can be provided in formats compatible for third-party systems." The partnership with Roy Hill and the upcoming 2019 product launch will fur- ther establish Maptek as a supplier of viable machine-learning-based solutions that enable advancements in automation in mining, Sullivan said. "There's a lot of 'buzzword bingo' in the market, which makes it difficult to separate the hype from reality, especially around machine learn- ing," he said. "This work puts a peg in the ground from Maptek to say we're doing this now, and we have a product that works." Full Spectrum Clarity Approaching a year ago, a satellite the size of a shoebox operated by a so-called asteroid mining company completed its first mission, which was basically to test its equipment. Among the gizmos tri- aled was hyperspectral imaging tech that could determine the mineralogical make- up of hurtling space bodies. About the same time, Hexagon Mining was working with the University of Arizo- na's Lowell Institute for Mineral Research and Headwall Photonics at deploying sim- ilar tech in the pit to assist in mapping orebodies, highwalls and leach pads. The results were promising. Hyperspectral imaging, according to Johnny Lyons-Baral, product manager, Hexagon Mining, could also have a bright future underground, which he hopes re- search will prove. "It allows you to get a really thorough characterization of the chemistry of an underground drift," he said. "You can see clearly and charac- terize most of your geologic, mineralogic alteration classifications." In recent years, hyperspectral imaging solutions have been deployed by space exploration companies and agencies to determine the dominant elements on dis- tant planets. "They are aiming out there to see which spectral peaks pop out," Ly- ons-Baral said. It has also been deployed to satellites to capture data on space and earth weath- er and earth geology. More down to earth, the scanners have been trialed on tripods, attached to vehicles and equipment, sus- pended over conveyors, and lugged aloft by drones. The applications range broadly but the goal is the same: to capture an electro- magnetic signature of something. "For geology we are looking at minerology," Lyons-Baral said. "Really, we are looking at the chemistry, which is why hyperspec- tral works well," he said. "We are going into the near-infrared and the short-wave infrared waveband range, up to about 2,500 nanometers for the spectra." The typical scanner captures a contin- uous measurement for a range of bands. For example, it generates a reflectance count for the 10-nanometer band, one for the 20-nanometer band, and so on. "Once you are done you plot it out across a graph," Lyons-Baral said. "You have your wavelengths down on the X axis and your refelctance magnitude on the Y axis giving you the spectral signatures." Ground truthing samples taken from the rock face scanned provide the information needed to classify mineralogy, alteration and lithology. After that, the data can be streamed into models and mine maps, or Data from hyperspectral scanning can be mixed with that from LIDAR to give texture and accuracy to models. (Photo: Hexagon Mining)

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