Mapping geological outcrops is a crucial part of mineral exploration, mine planning and ore extraction. With the advent of unmanned aerial systems (UASs) for rapid spatial and spectral mapping, opportunities arise in fields where traditional ground-based approaches are established and trusted, but fail to cover sufficient area or compromise personal safety. Multi-sensor UAS are a technology that change geoscientific research, but they are still not routinely used for geological mapping in exploration and mining due to lack of trust in their added value and missing expertise and guidance in the selection and combination of drones and sensors. To address these limitations and highlight the potential of using UAS in exploration settings, we present an UAS multi-sensor mapping approach based on the integration of drone-borne photography, multi- and hyperspectral imaging and magnetics. Data are processed with conventional methods as well as innovative machine learning algorithms and validated by geological field mapping, yielding a comprehensive and geologically interpretable product. As a case study, we chose the northern extension of the Siilinjärvi apatite mine in Finland, in a brownfield exploration setting with plenty of ground truth data available and a survey area that is partly covered by vegetation. We conducted rapid UAS surveys from which we created a multi-layered data set to investigate properties of the ore-bearing carbonatite-glimmerite body. Our resulting geologic map discriminates between the principal lithologic units and distinguishes ore-bearing from waste rocks. Structural orientations and lithological units are deduced based on high-resolution, hyperspectral image-enhanced point clouds. UAS-based magnetic data allow an insight into their subsurface geometry through modeling based on magnetic interpretation. We validate our results via ground survey including rock specimen sampling, geochemical and mineralogical analysis and spectroscopic point measurements. We are convinced that the presented non-invasive, data-driven mapping approach can complement traditional workflows in mineral exploration as a flexible tool. Mapping products based on UAS data increase efficiency and maximize safety of the resource extraction process, and reduce expenses and incidental wastes.

中文翻译：

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芬兰Siilinjärvi含碳酸盐岩露头的综合地质和地球物理地图，使用无人航空系统

测绘地质露头是矿产勘探，矿山规划和矿石开采的关键部分。随着用于快速空间和光谱制图的无人机系统（UAS）的出现，在建立和信任传统地面方法的领域中出现了机遇，但却无法覆盖足够的区域或危及人身安全。多传感器无人机系统是一种改变地球科学研究的技术，但由于对它们的附加值缺乏信任，并且在无人机和传感器的选择和组合方面缺乏专业知识和指导，因此它们仍未常规用于勘探和采矿的地质制图。为了解决这些限制并强调在探索环境中使用UAS的潜力，我们提出了一种基于无人机成像的集成的UAS多传感器映射方法，多光谱和高光谱成像与磁性。使用常规方法以及创新的机器学习算法对数据进行处理，并通过地质现场制图进行验证，从而获得了全面的，可在地质上解释的产品。作为案例研究，我们选择了芬兰Siilinjärvi磷灰石矿的北延，在一个棕地勘探环境中，该土地具有大量可用的地面真实数据，且调查区域部分被植被覆盖。我们进行了快速的UAS调查，从中我们创建了一个多层数据集，以研究含矿碳酸盐岩-闪锌矿体的特性。我们生成的地质图可区分主要岩性单位，并从废石中区分出矿藏。根据高分辨率推导了构造方向和岩性单位，高光谱图像增强点云。基于UAS的磁数据可以通过基于磁解释的建模来深入了解其地下几何形状。我们通过地面调查来验证我们的结果，包括岩石样本采样，地球化学和矿物学分析以及光谱点测量。我们坚信，提出的非侵入性，数据驱动的制图方法可以作为一种灵活的工具来补充矿物勘探中的传统工作流程。基于UAS数据的制图产品可提高效率并最大程度地提高资源提取过程的安全性，并减少支出和附带的浪费。地球化学和矿物学分析以及光谱点测量。我们坚信，提出的非侵入性，数据驱动的制图方法可以作为一种灵活的工具来补充矿物勘探中的传统工作流程。基于UAS数据的制图产品可提高效率并最大程度地提高资源提取过程的安全性，并减少支出和附带的浪费。地球化学和矿物学分析以及光谱点测量。我们坚信，提出的非侵入性，数据驱动的制图方法可以作为一种灵活的工具来补充矿物勘探中的传统工作流程。基于UAS数据的制图产品可提高效率并最大程度地提高资源提取过程的安全性，并减少支出和附带的浪费。