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Integrated Geological and Geophysical Mapping of a Carbonatite-Hosting Outcrop in Siilinjärvi, Finland, Using Unmanned Aerial Systems

机译:使用无人机的空中系统,综合地质和地球物理测绘芬兰Siilinjärvi的托管露头

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摘要

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.
机译:映射地质露头是矿产勘查,矿山规划和矿石中提取的重要组成部分。随着快速的空间和光谱测绘无人机系统(UAS的)的到来,机会出现在传统的基于地面的处理办法的和可信赖的领域,但没有足够的覆盖区域或妥协的人身安全。多传感器无人机是一种技术,它改变地球科学的研究,但他们仍然没有通常用于勘探和开采的地质填图,由于其附加值缺乏信任和缺少专业知识和指导,在选择和无人驾驶飞机和传感器的组合。为了解决这些限制,并强调在探索设置使用UAS的潜力,我们提出了一种基于无人机携带的摄影,多光谱和高光谱成像和磁一体化的UAS多传感器映射方法。数据与常规的方法以及新颖的机器学习算法来处理,并通过地质字段映射验证,得到了全面和地质可解释产物。作为一个案例研究中,我们选择了在芬兰锡林耶尔维磷灰石矿的北延,与大量的地面实况数据可用,并且部分地植被覆盖勘测区域棕地探索设置。我们进行了从我们创建了一个多层次的数据集,调查含矿碳酸盐-glimmerite体的性能迅速UAS调查。主要岩性单元和区分之间我们得到的地质图判别含矿从废石。结构取向和岩性单位基于高分辨率,高光谱图像增强点云推断。基于UAS磁性数据允许通过基于磁解释知识模拟洞察他们的地下构造几何形态。我们通过验证地面测量我们的研究结果包括岩石标本采样,地球化学和矿物学分析和光谱点测量。我们相信,所提出的非侵入性的,数据驱动的映射方法可以补充矿产勘查工作流程,传统作为一种灵活的工具。基于无人机系统的数据提高效率和资源开采过程中提高安全性测绘产品,并减少开支和附带废物。

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