Data-driven approaches to mineral exploration

Dave Cole1

1CSIRO, Docklands, Australia

 

The rate of new major mineral discoveries within Australia is decreasing as most deposits easily identifiable through existing techniques have already been found. At the same time the amount of available information relevant to mineral exploration is increasing. Machine learning techniques have potential to address both these problems by incorporating all available data and identifying complex patterns not easily discernible by traditional approaches. Such techniques can provide additional insight to geologists to compliment their existing knowledge and expertise and can help inform better decision making. This talk will briefly overview some examples of data-driven approaches applied to problems relating to mineral exploration.


Biography:

Dave Cole is a software developer and research engineer with an interest in large scale systems of automation, data fusion, and predictive modelling. He has a Ph.D. from the Australian Centre for Field Robotics at the University of Sydney and over a decades experience developing solutions to complex problems of automation involving sensor data in outdoor environments. His work includes: developing control systems and sensor fusion algorithms for networked robotic systems; development of mine automation and visualisation software for controlling autonomous mining equipment, mine safety monitoring, asset optimisation, and real-time data integration; applying machine learning algorithms to various geoscience problems from small scale rock characterisation to large scale geological modelling. He currently works within CSIRO’s Data61 where his focus is on applying data-driven algorithms to problems within the mining and exploration industry.

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