Application of Pattern Recognition Techniques to Monitoring-While-Drilling on a Rotary Electric Blasthole Drill at an Open-Pit Coal Mine

This thesis investigates the application of pattern recognition techniques to rock type recognition using monitoring-while-drilling data. The research is focused on data from a large electric blasthole drill operating in an open-pit coal mine. Pre-processing and normalization techniques are applied to minimize potential misclassification issues. Both supervised and unsupervised learning is employed in the classifier design: back-propagation neural networks are used for the supervised learning, while self-organizing maps are used for unsupervised learning. A variety of combinations of drilling data and geophysical data are investigated as inputs to the classifiers. The outputs from these classifiers are evaluated relative to the rock classification made by a commercially available rock type recognition system, as well as relative to independent labelling by a geologist. Classifier performance is improved when drilling data used as inputs are augmented with geophysical data inputs. By using supervised learning with both drilling and geophysical data as inputs, the misclassification of coal, as well as of the non-coal rock types, is reduced compared to results of current commercial recognition methods. Moreover, rock types which were not detected by the previous methods were successfully classified by the supervised models.

Department dc:contributor.department

Mining Engineering

Year dc:date.issued

2007

Author dc:creator

• Martin Gonzalez, Jorge Eduardo Jose

Advisor dc:contributor.supervisor

• Daneshmend, Laeeque K.

• neural network
• blasthole drilling monitoring

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1974/924

OAI identifier oai:identifier

oai:queensu.scholaris.ca:1974/924