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 sedimentary 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

This survey article provides a synopsis of some of the engineering problems, technological innovations, robotic development, and automation efforts encountered in the mining industry, particularly in the Pilbara iron ore region of Western Australia. The goal is to paint the technology landscape and highlight issues relevant to an engineering audience to raise awareness of artificial intelligence (AI) and automation trends in mining. It assumes the reader has no prior knowledge of mining and builds context gradually through focused discussion and short summaries of common open-pit mining operations. The principal activities that take place may be categorized in terms of resource development and mine, rail, and port operations (Figure 1). From mineral exploration to ore shipment, there are roughly nine steps in between (Figure 2). These include geological assessment, mine planning and development, production drilling and assaying, blasting and excavation, transportation of ore and waste, crushing and screening, stockpiling and loading out, rail network distribution, and ore car dumping. The objective is to describe these processes and provide insights on some of the challenges/opportunities from the perspective of a decade-long industry–university R&D partnership.

This article presents a Bayesian framework for manipulating mesh surfaces with the aim of improving the positional integrity of the geological boundaries that they seek to represent. The assumption is that these surfaces, created initially using sparse data, capture the global trend and provide a reasonable approximation of the stratigraphic, mineralization, and other types of boundaries for mining exploration, but they are locally inaccurate at scales typically required for grade estimation. The proposed methodology makes local spatial corrections automatically to maximize the agreement between the modeled surfaces and observed samples. Where possible, vertices on a mesh surface are moved to provide a clear delineation, for instance, between ore and waste material across the boundary based on spatial and compositional analysis using assay measurements collected from densely spaced, geo-registered blast holes. The maximum a posteriori (MAP) solution ultimately considers the chemistry observation likelihood in a given domain. Furthermore, it is guided by an a priori spatial structure that embeds geological domain knowledge and determines the likelihood of a displacement estimate. The results demonstrate that increasing surface fidelity can significantly improve grade estimation performance based on large-scale model validation.