Machine learning applied to drilling in open pit mines

The project involves identifying changes in mineralization during the drilling of the blast holes. During drilling, an experienced driller is able, to a certain extent, to detect signals that indicate that a zone change is occurring: vibration in the cabin, rotation rate, etc. The aim of this research project is to use data collected by the various sensors installed on the drill (specific energy, rotation rate, penetration rate, horizontal and vertical vibrations) to determine patterns among these data which would make it possible to identify a zone change and eventuallly automate this process. Machine Learning techniques are proposed to identify these patterns. The detection of these geological changes is particularly important in coal mines to stop drilling just before reaching the coal bed. This is to prevent blasting from fracturing the coal bed.

Faculty Supervisor:

Michel Gamache

Student:

Gilles Zagré

Partner:

Peck Tech Consulting Ltd

Discipline:

Mathematics

Sector:

Mining and quarrying

University:

École Polytechnique de Montréal