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Abstract This paper describes an application of adaptive sampling to geology modeling with a view of improving the operational cost and efficiency in certain surface mining applications. The objectives are to minimize the number of blast holes drilled into, and the accidental penetrations of, the geological boundary of interest. These objectives are driven by economic considerations as the cost is, firstly, directly proportional to the number of holes drilled and secondly, related to the efficiency of target material recovery associated with excavation and blast damage. The problem formulation is therefore motivated by the incentive to learn more about the lithology and drill less. The principal challenge with building an accurate surface model is that the sedimentary rock mass is coarsely sampled by drilling exploration holes which are typically a long distance apart. Thus, interpolation does not capture adequately local changes in the underlying geology. With the recent advent of consistent and reliable real-time identification of geological boundaries under field conditions using measure-while-drilling data, we pose the local model estimation problem in an adaptive sampling framework. The proposed sampling strategy consists of two phases. First, blast-holes are drilled to the geological boundary of interest, and their locations are adaptively selected to maximize utility in terms of the incremental improvement that can be made to the evolving spatial model. The second phase relies on the predicted geology and drills to an expert based pre-specified standoff distance from the geological boundary of interest, to optimize blasting and minimize its damage. Using data acquired from a coal mine survey bench in Australia, we demonstrate that adaptively choosing blast-holes in Phase 1 can minimize the total number of holes drilled to the top of the coal seam, as opposed to random hole selection, whilst optimizing blasting by maintaining a reasonable compromise in the error in the stopping distances from the seam. We also show that adaptive sampling requires, for accurate estimation, only a fraction of the holes that were initially drilled for this particular dataset.

@Article{Ahsan2015, Title = {Adaptive sampling applied to blast-hole drilling in surface mining }, Author = {Nasir Ahsan and Steven Scheding and Sildomar T. Monteiro and Raymond Leung and Charles McHugh and Danielle Robinson}, Journal = {International Journal of Rock Mechanics and Mining Sciences }, Year = {2015}, Month = {Mar.}, Number = {0}, Pages = {244--255}, Volume = {75}, Abstract = {Abstract This paper describes an application of adaptive sampling to geology modeling with a view of improving the operational cost and efficiency in certain surface mining applications. The objectives are to minimize the number of blast holes drilled into, and the accidental penetrations of, the geological boundary of interest. These objectives are driven by economic considerations as the cost is, firstly, directly proportional to the number of holes drilled and secondly, related to the efficiency of target material recovery associated with excavation and blast damage. The problem formulation is therefore motivated by the incentive to learn more about the lithology and drill less. The principal challenge with building an accurate surface model is that the sedimentary rock mass is coarsely sampled by drilling exploration holes which are typically a long distance apart. Thus, interpolation does not capture adequately local changes in the underlying geology. With the recent advent of consistent and reliable real-time identification of geological boundaries under field conditions using measure-while-drilling data, we pose the local model estimation problem in an adaptive sampling framework. The proposed sampling strategy consists of two phases. First, blast-holes are drilled to the geological boundary of interest, and their locations are adaptively selected to maximize utility in terms of the incremental improvement that can be made to the evolving spatial model. The second phase relies on the predicted geology and drills to an expert based pre-specified standoff distance from the geological boundary of interest, to optimize blasting and minimize its damage. Using data acquired from a coal mine survey bench in Australia, we demonstrate that adaptively choosing blast-holes in Phase 1 can minimize the total number of holes drilled to the top of the coal seam, as opposed to random hole selection, whilst optimizing blasting by maintaining a reasonable compromise in the error in the stopping distances from the seam. We also show that adaptive sampling requires, for accurate estimation, only a fraction of the holes that were initially drilled for this particular dataset.}, Doi = {10.1016/j.ijrmms.2015.01.009}, Gsid = {F1b5ZUV5XREC}, ISSN = {1365-1609}, Keywords = {Measure-while-drilling, Surface mining, Adaptive sampling, Blast-hole design optimization, Geological boundary detection, Mine automation}, Owner = {stmeee}, Timestamp = {2015.04.29}, Url = {http://www.sciencedirect.com/science/article/pii/S1365160915000167} }

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