Learning 3D geological structure from drill-rig sensors for automated mining
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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Analysis of drill performance data provides a powerful method for estimating subsurface geology. While there have been studies relating such measurement-while-drilling (MWD) parameters to rock properties, none of them has attempted to model context, that is, to associate local measurements with measurements obtained in neighbouring regions. This paper proposes a novel approach to infer geology from drill measurements by incorporating spatial relationships through a Conditional Random Field (CRF) framework. A boosting algorithm is used as a local classifier mapping drill measurements to corresponding geological categories. The CRF then uses this local information in conjunction with neighbouring measurements to jointly reason about their categories. Model parameters are learned from training data by maximizing the pseudo-likelihood. The probability distribution of classified borehole sections is calculated using belief propagation. We present experimental results of applying the method to MWD data collected from a semi-autonomous drill rig at an iron ore mine in Western Australia.