ALBERT

Description: Geological objects are characterized by a high complexity inherent to a strong compositional variability at all scales and usually unclear class boundaries. Therefore, dedicated processing schemes are required for the analysis of such data for mineralogical mapping. On the other hand, the variety of optical sensing technology reveals different data attributes and therefore multi-sensor approaches are adapted to solve such complicated mapping problems. In this paper, we devise an adapted multi-optical sensor fusion (MOSFus) workflow which takes the geological characteristics into account. The proposed processing chain exhaustively covers all relevant stages, including data acquisition, preprocessing, feature fusion, and mineralogical mapping. The concept includes (i) a spatial feature extraction based on morphological profiles on RGB data with high spatial resolution, (ii) a specific noise reduction applied on the hyperspectral data that assumes mixed sparse and Gaussian contamination, and (iii) a subsequent dimensionality reduction using a sparse and smooth low rank analysis. The feature extraction approach allows one to fuse heterogeneous data at variable resolutions, scales, and spectral ranges and improve classification substantially. The last step of the approach, an SVM classifier, is robust to unbalanced and sparse training sets and is particularly efficient with complex imaging data. We evaluate the performance of the procedure with two different multi-optical sensor datasets. The results demonstrate the superiority of this dedicated approach over common strategies.

Description: 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.

Description: The exposure of metal sulfides to air or water, either produced naturally or due to mining activities, can result in environmentally damaging acid mine drainage (AMD). This needs to be accurately monitored and remediated. In this study, we apply high-resolution unmanned aerial system (UAS)-based hyperspectral mapping tools to provide a useful, fast, and non-invasive method for the monitoring aspect. Specifically, we propose a machine learning framework to integrate visible to near-infrared (VNIR) hyperspectral data with physicochemical field data from water and sediments, together with laboratory analyses to precisely map the extent of acid mine drainage in the Tintillo River (Spain). This river collects the drainage from the western part of the Rio Tinto massive sulfide deposit and discharges large quantities of acidic water with significant amounts of dissolved metals (Fe, Al, Cu, Zn, amongst others) into the Odiel River. At the confluence of these rivers, different geochemical and mineralogical processes occur due to the interaction of very acidic water (pH 2.5–3.0) with neutral water (pH 7.0–8.0). This complexity makes the area an ideal test site for the application of hyperspectral mapping to characterize both rivers and better evaluate contaminated water bodies with remote sensing imagery. Our approach makes use of a supervised random forest (RF) regression for the extended mapping of water properties, using the samples collected in the field as ground-truth and training data. The resulting maps successfully estimate the concentration of dissolved metals and related physicochemical properties in water, and trace associated iron species (e.g., jarosite, goethite) within sediments. These results highlight the capabilities of UAS-based hyperspectral data to monitor water bodies in mining environments, by mapping their hydrogeochemical properties, using few field samples. Hence, we have demonstrated that our workflow allows the rapid discrimination and mapping of AMD contamination in water, providing an essential basis for monitoring and subsequent remediation.

Description: With the recurring interest in rare earth elements (REEs), laser-induced fluorescence (LiF) may provide a powerful tool for their rapid and accurate identification at different stages along their value chain. Applications to natural materials such as minerals and rocks could complement the spectroscopy-based toolkit for innovative, non-invasive exploration technologies. However, the diagnostic assignment of detected emission lines to individual REEs remains challenging because of the complex composition of natural rocks in which they can be found. The resulting mixed spectra and the large amount of data generated demand automated approaches of data evaluation, especially in mapping applications such as drill core scanning. LiF reference data provide the solution for robust REE identification, yet they usually remain in the form of tables of published emission lines. We show that a complete reference spectra library could open manifold options for innovative automated analysis. We present a library of high-resolution LiF reference spectra using the Smithsonian rare earth phosphate standards for electron microprobe analysis. We employ three standard laser wavelengths (325, 442, 532 nm) to record representative spectra in the UV-visible to near-infrared spectral range (340–1080 nm). Excitation at all three laser wavelengths yielded characteristic spectra with distinct REE-related emission lines for EuPO4, TbPO4, DyPO4 and YbPO4. In the other samples, the high-energy excitation at 325 nm caused unspecific, broad-band defect emissions. Here, lower-energy laser excitation is shown to be successful for suppressing non-REE-related emission. At 442 nm excitation, REE reference spectra depict the diagnostic emission lines of PrPO4, SmPO4 and ErPO4. For NdPO4 and HoPO4 the most efficient excitation was achieved with 532 nm. Our results emphasise the possibility of selective REE excitation by changing the excitation wavelength according to the suitable conditions for individual REEs. Our reference spectra provide a database for the transparent and reproducible evaluation of REE-bearing rocks. The LiF spectral library is available at zenodo.org and the registered DOI https://doi.org/10.5281/zenodo.4054606 (Fuchs et al., 2020). Primarily addressing the raw material exploration sector, it aids particularly the development of advanced data processing routines for LiF analysis but can also support further research on the REE luminescence in natural rocks or artificial compounds. It gives access to traceable data for the comparison of emission line positions, emission line intensity ratios and splitting into emission line sub-levels or can be used as reference or training data for automated approaches of component assignment.

Description: Rare earth elements (REEs) supply is important to ensure the energy transition, e-mobility and ultimately to achieve the sustainable development goals of the United Nations. Conventional exploration techniques usually rely on substantial geological field work including dense in-situ sampling with long delays until provision of analytical results. However, this approach is limited by land accessibility, financial status, climate and public opposition. Efficient and innovative methods are required to mitigate these limitations. The use of lightweight unmanned aerial vehicles (UAVs) provides a unique opportunity to conduct rapid and non-invasive exploration even in socially sensitive areas and in relatively inaccessible locations. We employ drones with hyperspectral sensors to detect REEs at the earth’s surface and thus contribute to a rapidly evolving field at the cutting edge of exploration technologies. We showcase for the first time the direct mapping of REEs with lightweight hyperspectral UAV platforms. Our solution has the advantage of quick turn-around times (〈 1 d), low detection limits (〈 200 ppm for Nd) and is ideally suited to support exploration campaigns. This procedure was successfully tested and validated in two areas: Marinkas Quellen, Namibia, and Siilinjärvi, Finland. This strategy should invigorate the use of drones in exploration and for the monitoring of mining activities.

Description: The traditional study of palaeoseismic trenches, involving logging, stratigraphic and structural interpretation, can be time consuming and affected by biases and inaccuracies. To overcome these limitations, a new workflow is presented that integrates infrared hyperspectral and photogrammetric data to support field-based palaeoseismic observations. As a case study, this method is applied on two palaeoseismic trenches excavated across a post-glacial fault scarp in northern Finnish Lapland. The hyperspectral imagery (HSI) is geometrically and radiometrically corrected, processed using established image processing algorithms and machine learning approaches, and co-registered to a structure-from-motion point cloud. HSI-enhanced virtual outcrop models are a useful complement to palaeoseismic field studies as they not only provide an intuitive visualisation of the outcrop and a versatile data archive, but also enable an unbiased assessment of the mineralogical composition of lithologic units and a semi-automatic delineation of contacts and deformational structures in a 3D virtual environment. Résumé L'étude traditionnelle des tranchées paléosismiques, impliquant l'enregistrement des coupes et l'interprétation stratigraphique et structurelle, peut prendre beaucoup de temps et être entachée de biais et d'inexactitudes. Pour surmonter ces limites, une nouvelle méthodologie est présentée, intégrant des données photogrammétriques et hyperspectrales infrarouges en appui aux observations paléosismiques de terrain. Comme étude de cas, cette méthode est appliquée à deux tranchées paléosismiques creusées à travers un escarpement de faille post-glaciaire dans le nord de la Laponie finlandaise. L'imagerie hyperspectrale (HSI) est corrigée géométriquement et radiométriquement, traitée à l'aide d'algorithmes classiques de traitement d'images et d'apprentissage machine, et recalée sur un nuage de points photogrammétrique. Les modèles virtuels d'affleurements améliorés par HSI constituent un complément utile aux études paléosismiques de terrain, car ils fournissent non seulement une visualisation intuitive de l'affleurement et une archive de données facile d'emploi, mais permettent également une évaluation non biaisée de la composition minéralogique d'unités lithologiques ainsi qu'une délimitation semi-automatique des contacts et des structures de déformation dans un environnement virtuel 3D.