Acquiring information about the spatial distribution of ore grade in the subsurface is essential for exploring and discovering mineral resources. This information is derived commonly from the geochemical analysis carried out on drill core samples, which allows the quantification of the concentration of ore elements. However, these surveys are generally time-consuming and expensive, usually leading to information at a low spatial resolution due to large sampling intervals. The use of hyperspectral systems in the mining industry to characterize and quantify minerals in drill cores is increasing due to their efficiency and fast turnaround time. Here, we propose the use of convolutional neural networks on hyperspectral data to estimate Cu concentration in drill cores at the Olympic Dam iron oxide copper-gold deposit. The Cu concentration data obtained by drill core geochemical analysis and the mean spectra between the analyzed intervals obtained from hyperspectral data were used to train the machine learning model. The trained model was then used to estimate the Cu concentration of a test drill core, which was not used to train the model. In addition, the trained model was used to extrapolate the Cu concentration, at a centimetric spatial resolution, to the drill core intervals without geochemical analysis. Qualitative and quantitative evaluations of the results demonstrate the capabilities of the proposed method, which provided a root mean squared error of 0.48 for the estimation of Cu percentage along drill cores. The results indicate that the method could be beneficial for determining the spatial distribution of ore grade by supporting the selection of zones of interest where more detailed analyses are appropriate, reducing the number of samples needed to characterize and identify the ore zones, and assisting in the estimation of the volume with commercially viable ore, thereby potentially reducing the geochemical assays needed and decreasing the data acquisition time.

中文翻译：

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使用卷积神经网络根据高光谱数据估算矿石品位：澳大利亚奥林匹克大坝氧化铁铜金矿床案例研究

获取地下矿石品位的空间分布信息对于勘探和发现矿产资源至关重要。该信息通常源自对钻芯样本进行的地球化学分析，从而可以量化矿石元素的浓度。然而，这些调查通常耗时且昂贵，并且由于采样间隔较大，通常会导致空间分辨率较低的信息。由于高光谱系统效率高且周转时间短，采矿业中越来越多地使用高光谱系统来表征和量化钻芯中的矿物。在这里，我们建议在高光谱数据上使用卷积神经网络来估计奥林匹克大坝氧化铁铜金矿床钻芯中的铜浓度。通过钻芯地球化学分析获得的铜浓度数据和从高光谱数据获得的分析区间之间的平均光谱用于训练机器学习模型。然后使用经过训练的模型来估计测试钻芯的铜浓度，该测试钻芯不用于训练模型。此外，训练后的模型用于以厘米空间分辨率将铜浓度外推到钻芯间隔，而无需进行地球化学分析。结果的定性和定量评估证明了所提出方法的能力，该方法为估计沿钻芯的铜百分比提供了 0.48 的均方根误差。结果表明，该方法通过支持选择适合进行更详细分析的感兴趣区域、减少表征和识别矿石区域所需的样本数量以及协助确定矿石品位的空间分布，有利于确定矿石品位的空间分布。估算商业上可行的矿石的体积，从而可能减少所需的地球化学分析并减少数据采集时间。