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Automated core logging technology for geotechnical assessment; a study on core from the Cadia East porphyry deposit

Cassady L. Harraden, Matthew J. Cracknell, Lett James, Ron F. Berry, Ronell Carey and Anthony C. Harris
Automated core logging technology for geotechnical assessment; a study on core from the Cadia East porphyry deposit
Economic Geology and the Bulletin of the Society of Economic Geologists (May 2019) 114 (8): 1495-1511

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.


ISSN: 0361-0128
EISSN: 1554-0774
Coden: ECGLAL
Serial Title: Economic Geology and the Bulletin of the Society of Economic Geologists
Serial Volume: 114
Serial Issue: 8
Title: Automated core logging technology for geotechnical assessment; a study on core from the Cadia East porphyry deposit
Affiliation: University of Tasmania, Australia Research Council Industrial Transformation Research Hub, Transforming the Mining Value Chain, Hobart, Tasmania, Australia
Pages: 1495-1511
Published: 20190517
Text Language: English
Publisher: Economic Geology Publishing Company, Lancaster, PA, United States
References: 45
Accession Number: 2019-063804
Categories: Economic geology, geology of ore deposits
Document Type: Serial
Bibliographic Level: Analytic
Illustration Description: illus. incl. 3 tables
S37°30'00" - S28°15'00", E141°00'00" - E153°30'00"
Secondary Affiliation: Newcrest Mining, AUS, AustraliaCorescan Proprietary, AUS, Australia
Country of Publication: United States
Secondary Affiliation: GeoRef, Copyright 2019, American Geosciences Institute. Abstract, Copyright, Society of Economic Geologists. Reference includes data from GeoScienceWorld, Alexandria, VA, United States
Update Code: 201933
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