Automated coal seam detection using a modulated specific energy measure in a monitor-while-drilling context

This paper describes a novel measure called Modulated Specific Energy (SEM) which has been developed for the purpose of characterizing drilled material in open-pit coal mining. In Monitor-While-Drilling (MWD), the information available for coal detection are limited to a small set of drilling parameters that can be measured on a rotary drill rig. Despite this constraint, our analysis shows that MWD can still detect the top of the coal seam consistently without relying on geophysical data such as bulk density and natural gamma by using the SEM measure. The proposal utilizes a hypothesized link between a derived drill performance indicator (the rotation-to-thrust power ratio) and geomechanical properties of sedimentay rock strata (shear and compressive strengths) to increase the coal discriminative power of SEM relative to Teale's specific energy measure. Its efficacy is demonstrated using mutual information, a simple threshold strategy and an artificial neural network. The results show the SEM can detect the coal seam interface consistently with a greater margin for error, and overcome the problems of low specificity and high variability observed in existing MWD approaches. By reducing the detection uncertainty, the SEM is able to provide consistent feedback while drilling and eliminate trial-and-error. This makes coal mining processes more integrated and reliable, which in turn improves operational effectiveness and efficiency in coal recovery. (C) 2015 The Authors. Published by Elsevier Ltd.