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This webinar consists of three 15-minute mini-lectures followed by a 30-minute panel discussion.
Introduction
3:10 The Mining Cycle
Mini-lectures
3:50 Machine Learning as a Tool for Geologists | Antoine Cate, SRK Consulting
23:45 Generating Geological Models in Minutes Using Machine Learning | Steve Sullivan, Maptek
41:35 How Machine Learning Can Magnify Orebody Knowledge | Penny Stewart, Petra
Panel discussion
1:00:18 Q&A discussion introduction
1:00:23 What libraries can I use for drawing maps and creating prediction models in python?
1:02:33 How is it possible to know the uncertainty of the prediction maps?
1:03:44 What is the relation between density data and hotspot dimension when you apply predictive maps?
1:05:11 How do we actually prepare our data for machine learning?
1:08:45 How to deal with outliers?
1:13:48 Should data science financials be part of undergraduate science education?
1:15:27 In the machine learning environment, how do you deal with missing data in data sets?
1:23:00 What would you recommend for early career professionals entering this space to be productive and provide value into the industry?
This webinar was produced by the Society of Economic Geologists and sponsored by Maptek.
SRK’s Chris Woodfull and specialists from DeepIQ and BHP Exploration, presented a case study on prospectivity modelling at the Future of Mining conference.
Watch VideoThis webinar explores why grade distribution, optimising mining selectivity and controlling mining dilution and losses through good grade control practices is essential for achieving the mine plan.
Watch Video