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.

中文翻译：

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用于岩土评估的自动岩心测井技术：卡迪亚东斑岩矿床岩心研究

Cadia East 斑岩矿床位于澳大利亚新南威尔士州奥兰治以南约 20 公里处，含有丰富的铜和金资源。该资源位于 Forest Reefs Volcanics 内，在空间和时间上与 Cadia Intrusive Complex 相关。为提取矿石，该地下矿山目前采用块洞开采法。Cadia East 岩土工程模型为一系列数值和经验分析方法提供数据输入，这些方法构成了矿山设计的基础。这些数据为应力模型的构建、可塌陷性模型、地面支撑设计和碎裂分析提供了输入。该岩土工程模型包含两种常用的岩石分类系统，用于量化地面条件：(1) 岩体等级 (RMR) 和 (2) 岩石隧道质量指数 (Q 指数)。RMR 和 Q 指数是根据岩石质量指标 (RQD)、裂缝组数、裂缝粗糙度、裂缝蚀变和裂缝间距的估计计算得出的。地质学家和岩土工程师通过手动记录钻芯部分来收集用于进行这些估计的信息，这是一项耗时的任务，可能会导致数据不一致。现代自动岩心扫描技术提供了从更大的钻芯样本中快速收集数据的机会。这些自动岩心测井系统生成大量空间和光谱一致的数据，包括来自激光轮廓系统的钻芯表面模型。岩心表面模型用于从定向钻芯中提取裂缝位置、方向和粗糙度的详细测量值。这些数据与自动高光谱岩心测井系统的其他形态学和矿物学输出相结合，以系统地估计连续钻芯间隔内的 RMR 和 Q 指数。本研究的目标是开发一种概念验证方法，从定向钻芯收集的高光谱和激光地形数据中提取岩土工程指标参数。本案例研究使用来自 Cadia East 矿的高光谱数据来评估这些方法。结果表明，可以从自动岩心测井数据中提取有助于 RMR 和 Q 指数的形态学和矿物学参数。这种方法提供了一个机会，可以在非常耗时的规模上捕获一致的地质、矿物和岩土数据，而通过手动数据收集来实现。