Towards grade estimation via automated interpretation of geophysical boreholelogs

Abstract

Economic benefits can flow from geophysical logging at all stages of the mining cycle. The most commonly cited benefit is the substitution of delineation diamond drilling with cheaper percussion or reverse circulation drilling in cases where geophysical logs can substitute for core. This approach can deliver an attractive direct saving in drilling costs if drill metreage is unchanged, or a potentially greater indirect benefit from better resource delineation if more holes are drilled within the original drilling budget. Operational advantages of logging include data objectivity, speed of interpretation, and reduced core handling and analysis costs. Substitution of diamond drilling with percussion drilling is not always feasible. However, the amount of core drilling which can be foregone in favour of more economical drilling plus logging expands enormously if grade can be reliably inferred from petrophysical logs. For some ore systems a close correlation can exist between a petrophysical parameter and grade. More commonly, individual petrophysical grade estimates are less reliable than assays, but the overall correlation with grade may still be adequate for discrimination between grade ranges, for example, ore versus waste, or low-grade versus high-grade. In order to exploit the potential for petrophysical grade estimation in these cases, efficient means must be found to infer grade ranges from borehole logs. An automated interpretation tool, LogTrans, has been developed for a geophysical log analysis. LogTrans performs rapid analysis of multi-parameter logs and expedites presentation of interpreted results in a form meaningful to mining engineers and geologists. The LogTrans algorithm exploits the contrasts in petrophysical signatures between different classes of rock, distinguished by lithology, grade, mechanical properties, or a combination of characteristics. Interpretation entails two stages; the first, is a statistical characterisation, where geophysical logs and core-based geoscientific data from a set of control holes are combined to form an attribute lookup table. The second stage statistical discrimination, examines the measured physical properties at a particular depth and compares them to the lookup table for a rock class assignment. In this paper we present two examples of petrophysical grade estimation, one from the Newlands Coal Mine and one from the Mount Isa underground copper operations.