The Phoenix mine and predecessor operations in north-central Nevada have produced an aggregate of 5.2 Moz of gold and 550 million pounds of copper from an Eocene-aged Au-Cu porphyry-related skarn. The complex skarn mineralogy intimately associated with ore-grade mineralization poses significant challenges to blasting, mining, comminution, and process operations. These challenges are rooted in highly variable silicate mineralogy, which manifests as fine-grained, submillimeter grain-size, generally green colored rocks that inhibit accurate identification in the field. Prior to this study, all mineralogical data utilized in Phoenix mine ore control were sourced from blast hole cuttings mapped by ore control geologists in the field—the standard practice at many modern mine sites. At Phoenix, a direct link between mineralogy and mill performance was recognized; however, mineralogical data captured in the field was not sufficient to optimize process operations.To address this, it was determined that analytical work was necessary to quantify fine-grained mineralogy of variable ore types. A visible-near and short-wave infrared (VNIR-SWIR) hyperspectral imaging system provided the ideal tool, as it allows near real-time mineralogical data acquisition and semiquantitative determination of mineral abundances. Multiple iterative studies were conducted to prove that hyperspectral imaging of Phoenix ore types provides results suitable for process optimization. This six-month study described here included hyperspectral imaging of 3,008 blast hole cuttings samples from three pits, and 877 crusher feed, rougher feed, and rougher tails samples. Hyperspectral feature extractions derived from mill samples were paired with associated mill performance data and used to build predictive Au-Cu recovery, grade, and throughput models using multiple linear regression, partial least squares, and deep learning techniques with R-correlation values to observed data of 0.56 to 0.71. Blast hole hyperspectral data were then applied to recovery, grade, and throughput models to calculate predicted recoveries and throughputs that were spatially kriged with excellent correlations to geologic features.The application of VNIR-SWIR hyperspectral imaging to blast hole cuttings is a powerful predictive and diagnostic geometallurgical tool in operations where silicate mineralogy has a strong impact on process operations.

中文翻译：

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高光谱成像在地质冶金中的应用：利用爆破孔矿物学预测内华达州菲尼克斯矿的金铜回收率和产量

内华达州中北部的Phoenix矿山及其前身业务，是由始新世时代的与Au-Cu斑岩有关的矽卡岩共产生5.2 Moz的黄金和5.5亿磅的铜。与矿石级矿化密切相关的复杂矽卡岩矿物学对爆破，采矿，粉碎和加工操作提出了重大挑战。这些挑战源于高度可变的硅酸盐矿物学，这表现为粒度细，亚毫米级的颗粒，通常是绿色的岩石，阻碍了现场的准确识别。在进行这项研究之前，Phoenix矿控矿中使用的所有矿物学数据均来自该矿场的控矿地质学家绘制的爆破孔岩屑，这是许多现代矿场的标准做法。在凤凰城 认识到矿物学和轧机性能之间的直接联系；然而，现场捕获的矿物学数据不足以优化工艺操作。为解决此问题，已确定必须进行分析工作以量化可变矿石类型的细粒矿物学。可见光和短波红外（VNIR-SWIR）高光谱成像系统提供了理想的工具，因为它允许近实时的矿物学数据采集和矿物质丰度的半定量测定。进行了多次迭代研究，以证明Phoenix矿石类型的高光谱成像可提供适合过程优化的结果。这里描述的为期六个月的研究包括对来自三个矿井的3008个爆破孔岩屑样品以及877个破碎机进料，粗饲料和粗尾样品进行高光谱成像。将源自工厂样品的高光谱特征提取与相关工厂性能数据配对，并使用多元线性回归，偏最小二乘和深度学习技术（与观测数据具有R相关值）来建立预测的金铜回收，品位和产量模型为0.56至0.71。然后将爆破孔高光谱数据应用于采收率，品位和生产能力模型，以计算预测的采收率和产量，这些采收率和产量在空间上与地质特征具有极佳的相关性.VNIR-SWIR高光谱成像在爆破孔岩屑中的应用是强大的预测和诊断工具硅酸盐矿物学对工艺操作有重大影响的操作中的地质冶金工具。