The analysis of drill-core samples is one of the most important steps in the mining industry for the exploration and discovery of mineral resources. Geochemical assays are a common approach to represent the abundance of different chemical elements and aid at quantifying the concentrations of the important ore accumulations. However, their acquisition is time-consuming and usually averages of long core portions. Hyperspectral data are increasingly being used in the mining industry to complement the analysis of drill-cores due to their efficiency and fast turn-around time. Moreover, hyperspectral imaging is a technique able to provide data with high spatial resolution. In this article, we propose to integrate the complementary information derived from hyperspectral and geochemical data via a superpixel-based machine learning framework. This framework considers the difference in spatial resolution through segmentation. We extract labels from the geochemical assays and select, from the hyperspectral data, representative samples for each measurement. A supervised machine learning classification (composite kernel support vector machine) is then used to extrapolate the elements relative abundance to the entire core length. We propose an innovative integration of hyperspectral data covering different regions of the electromagnetic spectrum in a kernel-based framework to facilitate the identification of a larger amount of elements. A qualitative and quantitative evaluation of the results demonstrates the capabilities of the proposed method, which provides approximately 20% more accurate results than the pixel-based approach. Results also imply that the method could be beneficial for the reduction of geochemical assays needed for the detailed analysis of the cores.

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基于超像素的机器学习框架中的钻芯高光谱和地球化学数据集成

钻芯样品的分析是采矿业勘探和发现矿产资源的最重要步骤之一。地球化学分析是表示不同化学元素丰度并有助于量化重要矿石堆积物浓度的常用方法。然而，它们的获取是耗时的并且通常是长核心部分的平均值。由于高光谱数据效率高且周转时间短，因此采矿业越来越多地使用高光谱数据来补充钻芯分析。此外，高光谱成像是一种能够提供具有高空间分辨率的数据的技术。在本文中，我们建议通过基于超像素的机器学习框架整合来自高光谱和地球化学数据的补充信息。该框架通过分割来考虑空间分辨率的差异。我们从地球化学分析中提取标签，并从高光谱数据中为每次测量选择具有代表性的样本。然后使用监督机器学习分类（复合核支持向量机）将元素相对丰度外推到整个核心长度。我们建议在基于内核的框架中对覆盖电磁频谱不同区域的高光谱数据进行创新集成，以促进对大量元素的识别。结果的定性和定量评估证明了所提出方法的能力，与基于像素的方法相比，该方法提供了大约 20% 的准确结果。