Abstract

The Cadia East porphyry deposit, located approximately 20 km south of Orange, New South Wales, Australia, contains a significant resource of copper and gold. This resource is hosted within the Forest Reefs Volcanics and is spatially and temporally associated with the Cadia Intrusive Complex. To extract ore, the underground mine currently uses the block cave mining method. The Cadia East geotechnical model provides data inputs into a range of numerical and empirical analysis methods that make up the foundation for mine design. These data provide input into the construction of stress models, caveability models, ground support design, and fragmentation analysis. This geotechnical model encompasses two commonly used rock classification systems that quantify ground conditions: (1) rock mass rating (RMR) and (2) rock tunneling quality index (Q index). The RMR and Q index are calculated from estimates of rock quality designation (RQD), number of fracture sets, fracture roughness, fracture alteration, and fracture spacing. Geologists and geotechnical engineers collect information used to produce these estimates by manually logging sections of drill core, a time-consuming task that can result in inconsistent data.

Modern automated core scanning technologies offer opportunities to rapidly collect data from larger samples of drill core. These automated core logging systems generate large volumes of spatially and spectrally consistent data, including a model of the drill core surface from a laser profiling system. Core surface models are used to extract detailed measurements of fracture location, orientation, and roughness from oriented drill core. These data are combined with other morphological and mineralogical outputs from automated hyperspectral core logging systems to estimate RMR and the Q index systematically over contiguous drill core intervals. The goal of this study was to develop a proof-of-concept methodology that extracts geotechnical index parameters from hyperspectral and laser topographic data collected from oriented drill core.

Hyperspectral data from the Cadia East mine were used in this case study to assess the methods. The results show that both morphological and mineralogical parameters that contribute to the RMR and Q index can be extracted from the automated core logging data. This approach provides an opportunity to capture consistent geologic, mineralogical, and geotechnical data at a scale that is too time-consuming to achieve via manual data collection.

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