Engineering & Mining Journal

FEB 2019

Engineering and Mining Journal - Whether the market is copper, gold, nickel, iron ore, lead/zinc, PGM, diamonds or other commodities, E&MJ takes the lead in projecting trends, following development and reporting on the most efficient operating pr

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Page 39 of 75

RESOURCE MODELLING 38 E&MJ • FEBRUARY 2019 The resource modelling solution space il- lustrates this as well as any. Currently the drivers for change link back to declining ore grades and new discoveries increas- ingly situated in difficult locales. "A lot of resources now are trickier to access, or costly or more risky," a resource mod- elling software expert, Nick Fogarty said. Risk drives the miners to innovate and it compels them to seek innovative solutions from suppliers. It also drives them to seek to do "more with less," Fogarty said. "What companies are realizing is that to drive down the cost of acquisition of resource isn't re- ducing, it is increasing, which means that there is a higher focus on productivity in the acquisition of data. The ability to come to the right decision more quickly means that a company may not drill that drill hole or may proceed in areas where they may not have historically." When you throw in the pervasive cli- mate of uncertainty that followed the de- cline of the super cycle, these days could prove to be boom times for tech compa- nies that can promise accurate models. Seizing the opportunities now available, several suppliers are promoting solutions that they say offer clarity, calm nerves and speed tough decisions. Feed the Need for Speed Maptek reported Roy Hill in the Pilbara will adopt its new resource modelling and reporting solution starting in Q1 2019. "Roy Hill geologists will provide valuable feedback to the ongoing research and de- velopment, all of which is being undertak- en by Maptek," Steve Sullivan, geologist and product manager, Maptek, said. Maptek described the system as a deep- learning solution, powered by Maptek's machine-learning engine. The company envisions it revolutionizing the grade es- timation process by allowing geologists to transform database information to resource reports in dramatically reduced time. At the Western Australian iron ore mine, resource database information will be uploaded to a Maptek cloud server. There, "data analysis leads to automat- ed assignment of estimation parameter settings, followed by geological domain interpretation, grade interpolation and uncertainty analysis," Maptek reported. Resource modelling and post-process analysis using the Maptek machine-learn- ing engine will be available in Maptek Workbench applications, like Vulcan. The results will then be downloaded in standard Maptek block-model format for resource reporting and collaboration with other users of the geological resource model, such as geotechnical, mine plan- ning and mine scheduling engineers. "The resulting block model file is a bina- ry file with .bmf extension," Sullivan said. "This format allows fast data access for the 3D visualization and reporting and integra- tion with geotechnical data and mine plan- ning and mine scheduling functions." The sub-blocked block model offers key visualization capabilities. It can be interrogated with standard analysis and validation tools provided in Vulcan 11, Sullivan said. "Data can be visualized as 2D slices, 3D blocks, 3D isosurfac- es, 2D charts as histograms, box plots, retrospective variograms, 2D swath plots comparing drill data with machine model and reserve reports generated," he said. The key benefits are accuracy, speed and ease of use, Maptek reported. The system features functions that en - able the user to dial in their acceptable margin of error, Sullivan said. Input data is validated prior to modelling and results are validated with standard variogram charting techniques. Sullivan said tests revealed the system architecture provides repeatable results within customer tolerances and expecta- tions. "In addition to modelling parame- ters, the uncertainty in the estimation of each parameter is provided to the end user for analysis and audit purposes," he said. "The results from the Maptek machine learning engine have been com- pared with the results, including uncer- tainty factors from existing techniques such as ordinary kriging," Sullivan said. "The results have been compatible." The results have also been generated in a fraction of the time normally required. In at least one trial, a resource report was Friendlier Models Try to Bare All, Faster With little room for error, miners seek resource modelling solutions that are quick, easy and accurate By Jesse Morton, Technical Writer Above, Maptek's deep-learning algorithm leverages drill hole data to quickly model grade distribution. (Photo: Maptek)

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