Identification of rare earth elements in synthetic and natural monazite and xenotime by visible-to-shortwave infrared reflectance spectroscopy

To support the role of proximal and remote sensing in geological rare earth element (REE) resource exploration, we studied the reflectance spectroscopy of synthetic single- and mixed-REE phosphate phases. Synthesis yielded monazite for the elements La to Gd, and xenotime for Dy to Lu and Y. Visible-to-shortwave infrared (350–2500 nm) reflectance spectra of synthetic single-REE monazites and xenotimes can be used to identify the ions responsible for the absorption features in natural monazites and xenotimes. Nd3+, Pr3+ and Sm3+ produce the main absorption features in monazites. In natural xenotime, Dy3+, Er3+, Ho3+ and Tb3+ ions cause the prevalent absorptions. The majority of the REE-related absorption features are due to photons exciting electrons within the 4f subshell of the trivalent lanthanide ions to elevated energy levels resulting from spin-orbit coupling. There are small (&lt; 20 nm) shifts in the wavelengths of these absorptions depending on the nature of the ligands. The energy levels are further split by crystal field effects, manifested in the reflectance spectra as closely spaced (∼ 5–20 nm) multiplets within the larger absorption features. Superimposed on the electronic absorptions are vibrational absorptions in the H2O molecule or within [OH]−, [CO3]2− and [PO4]3− functional groups, but so far only the carbonate-related spectral features seem usable as a diagnostic tool in REE-bearing minerals. Altogether, our study creates a strengthened knowledge base for detection of REE using reflectance spectroscopy and provides a starting point for the identification of REE and their host minerals in mineral resources by means of hyperspectral methods.</p

The Need for Accurate Pre-processing and Data Integration for the Application of Hyperspectral Imaging in Mineral Exploration

Die hyperspektrale Bildgebung stellt eine Schlüsseltechnologie in der nicht-invasiven Mineralanalyse dar, sei es im Labormaßstab oder als fernerkundliche Methode. Rasante Entwicklungen im Sensordesign und in der Computertechnik hinsichtlich Miniaturisierung, Bildauflösung und Datenqualität ermöglichen neue Einsatzgebiete in der Erkundung mineralischer Rohstoffe, wie die drohnen-gestützte Datenaufnahme oder digitale Aufschluss- und Bohrkernkartierung. Allgemeingültige Datenverarbeitungsroutinen fehlen jedoch meist und erschweren die Etablierung dieser vielversprechenden Ansätze. Besondere Herausforderungen bestehen hinsichtlich notwendiger radiometrischer und geometrischer Datenkorrekturen, der räumlichen Georeferenzierung sowie der Integration mit anderen Datenquellen. Die vorliegende Arbeit beschreibt innovative Arbeitsabläufe zur Lösung dieser Problemstellungen und demonstriert die Wichtigkeit der einzelnen Schritte. Sie zeigt das Potenzial entsprechend prozessierter spektraler Bilddaten für komplexe Aufgaben in Mineralexploration und Geowissenschaften.Hyperspectral imaging (HSI) is one of the key technologies in current non-invasive material analysis. Recent developments in sensor design and computer technology allow the acquisition and processing of high spectral and spatial resolution datasets. In contrast to active spectroscopic approaches such as X-ray fluorescence or laser-induced breakdown spectroscopy, passive hyperspectral reflectance measurements in the visible and infrared parts of the electromagnetic spectrum are considered rapid, non-destructive, and safe. Compared to true color or multi-spectral imagery, a much larger range and even small compositional changes of substances can be differentiated and analyzed. Applications of hyperspectral reflectance imaging can be found in a wide range of scientific and industrial fields, especially when physically inaccessible or sensitive samples and processes need to be analyzed. In geosciences, this method offers a possibility to obtain spatially continuous compositional information of samples, outcrops, or regions that might be otherwise inaccessible or too large, dangerous, or environmentally valuable for a traditional exploration at reasonable expenditure. Depending on the spectral range and resolution of the deployed sensor, HSI can provide information about the distribution of rock-forming and alteration minerals, specific chemical compounds and ions. Traditional operational applications comprise space-, airborne, and lab-scale measurements with a usually (near-)nadir viewing angle. The diversity of available sensors, in particular the ongoing miniaturization, enables their usage from a wide range of distances and viewing angles on a large variety of platforms. Many recent approaches focus on the application of hyperspectral sensors in an intermediate to close sensor-target distance (one to several hundred meters) between airborne and lab-scale, usually implying exceptional acquisition parameters. These comprise unusual viewing angles as for the imaging of vertical targets, specific geometric and radiometric distortions associated with the deployment of small moving platforms such as unmanned aerial systems (UAS), or extreme size and complexity of data created by large imaging campaigns. Accurate geometric and radiometric data corrections using established methods is often not possible. Another important challenge results from the overall variety of spatial scales, sensors, and viewing angles, which often impedes a combined interpretation of datasets, such as in a 2D geographic information system (GIS). Recent studies mostly referred to work with at least partly uncorrected data that is not able to set the results in a meaningful spatial context. These major unsolved challenges of hyperspectral imaging in mineral exploration initiated the motivation for this work. The core aim is the development of tools that bridge data acquisition and interpretation, by providing full image processing workflows from the acquisition of raw data in the field or lab, to fully corrected, validated and spatially registered at-target reflectance datasets, which are valuable for subsequent spectral analysis, image classification, or fusion in different operational environments at multiple scales. I focus on promising emerging HSI approaches, i.e.: (1) the use of lightweight UAS platforms, (2) mapping of inaccessible vertical outcrops, sometimes at up to several kilometers distance, (3) multi-sensor integration for versatile sample analysis in the near-field or lab-scale, and (4) the combination of reflectance HSI with other spectroscopic methods such as photoluminescence (PL) spectroscopy for the characterization of valuable elements in low-grade ores. In each topic, the state of the art is analyzed, tailored workflows are developed to meet key challenges and the potential of the resulting dataset is showcased on prominent mineral exploration related examples. Combined in a Python toolbox, the developed workflows aim to be versatile in regard to utilized sensors and desired applications

On the Feasibility of Imaging Carbonatite-Hosted Rare Earth Element Deposits Using Remote Sensing

Rare earth elements (REEs) generate characteristic absorption features in visible to shortwave infrared (VNIR-SWIR) reflectance spectra. Neodymium (Nd) has among the most prominent absorption features of the REEs and thus represents a key pathfinder element for the REEs as a whole. Given that the world’s largest REE deposits are associated with carbonatites, we present spectral, petrographic, and geochemical data from a predominantly carbonatitic suite of rocks that we use to assess the feasibility of imaging REE deposits using remote sensing. Samples were selected to cover a wide range of extents and styles of REE mineralization, and encompass calcio-, ferro- and magnesio-carbonatites. REE ores from the Bayan Obo (China) and Mountain Pass (United States) mines, as well as REE-rich alkaline rocks from the Motzfeldt and Ilímaussaq intrusions in Greenland, were also included in the sample suite. The depth and area of Nd absorption features in spectra collected under laboratory conditions correlate positively with the Nd content of whole-rock samples. The wavelength of Nd absorption features is predominantly independent of sample lithology and mineralogy. Correlations are most reliable for the two absorption features centered at ~744 and ~802 nm that can be observed in samples containing as little as ~1,000 ppm Nd. By convolving laboratory spectra to the spectral response functions of a variety of remote sensing instruments we demonstrate that hyperspectral instruments with capabilities equivalent to the operational Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) and planned Environmental Mapping and Analysis Program (EnMAP) systems have the spectral resolutions necessary to detect Nd absorption features, especially in high-grade samples with economically relevant REE accumulations (Nd > 30,000 ppm). Adding synthetic noise to convolved spectra indicates that correlations between Nd absorption area and whole-rock Nd content only remain robust when spectra have signal-to-noise ratios in excess of ~250:1. Although atmospheric interferences are modest across the wavelength intervals relevant for Nd detection, most REE-rich outcrops are too small to be detectable using satellite-based platforms with >30-m spatial resolutions. However, our results indicate that Nd absorption features should be identifiable in high-quality, airborne, hyperspectral datasets collected at meter-scale spatial resolutions. Future deployment of hyperspectral instruments on unmanned aerial vehicles could enable REE grade to be mapped at the centimeter scale across whole deposits

Drone-based Integration of Hyperspectral Imaging and Magnetics for Mineral Exploration

The advent of unoccupied aerial systems (UAS) as disruptive technology has a lasting impact on remote sensing, geophysics and most geosciences. Small, lightweight, and low-cost UAS enable researchers and surveyors to acquire earth observation data in higher spatial and spectral resolution as compared to airborne and satellite data. UAS-based applications range from rapid topographic mapping using photogrammetric techniques to hyperspectral and geophysical measurements of surface and subsurface geology. UAS surveys contribute to identifying metal deposits, monitoring of mine sites and can reveal arising environmental issues associated with mining. Further, affordable UAS technology will boost exploration data availability and expertise in the global south. This thesis investigates the application of UAS-based multi-sensor data for mineral exploration, in particular the integration of hyperspectral imagers, magnetometers and digital cameras (covering the visible red, green, blue light spectrum). UAS-based research is maturing, however the aforementioned methods are not unified effectively. RGB-based photogrammetry is used to investigate topography and surface texture. Image spectrometers measure mineral-specific surface signatures. Magnetometers detect geomagnetic field changes caused by magnetic minerals at surface and depth. The integration of such UAS sensor-based methods in this thesis augments exploration potential with non-invasive, high-resolution, safe, rapid and practical survey methods. UAS-based surveying acquired, processed and integrated data from three distinct test sites. The sites are located in Finland (Fe-Ti-V at Otanmäki; apatite at Siilinjärvi) and Greenland (Ni-Cu-PGE at Qullissat, Disko Island) and were chosen as geologically diverse areas in subarctic to arctic environments. Restricted accessibility, unfavourable atmospheric conditions, dark rocks, debris and vegetation cover and low solar illumination were common features. While the topography in Finland was moderately flat, a steep landscape challenged the Greenland field work. These restraints meant that acquisitions varied from site to site and how data was integrated and interpreted is dependent on the commodity of interest. Iron-based spectral absorption and magnetic mineral response were detected using hyperspectral and magnetic surveying in Otanmäki. Multi-sensor-based image feature detection and classification combined with magnetic forward modelling enabled seamless geologic mapping in Siilinjärvi. Detailed magnetic inversion and multispectral photogrammetry led to the construction of a comprehensive 3D model of magmatic exploration targets in Greenland. Ground truth at different intensity was employed to verify UAS-based data interpretations during all case studies. Laboratory analysis was applied when deemed necessary to acquire geologic-mineralogic validation (e.g., X-ray diffraction and optical microscopy for mineral identification to establish lithologic domains, magnetic susceptibility measurements for subsurface modelling), for example for trace amounts of magnetite in carbonatite (Siilinjärvi) and native iron occurrence in basalt (Qullissat). Technical achievements were the integration of a multicopter-based prototype fluxgate-magnetometer data from different survey altitudes with ground truth, and a feasibility study with a high-speed multispectral image system for fixed-wing UAS. The employed case studies transfer the experiences made towards general recommendations for UAS application-based multi-sensor integration. This thesis highlights the feasibility of UAS-based surveying at target scale (1–50 km2) and solidifies versatile survey approaches for multi-sensor integration.Ziel dieser Arbeit war es, das Potenzial einer Drohnen-basierten Mineralexploration mit Multisensor-Datenintegration unter Verwendung optisch-spektroskopischer und magnetischer Methoden zu untersuchen, um u. a. übertragbare Arbeitsabläufe zu erstellen. Die untersuchte Literatur legt nahe, dass Drohnen-basierte Bildspektroskopie und magnetische Sensoren ein ausgereiftes technologisches Niveau erreichen und erhebliches Potenzial für die Anwendungsentwicklung bieten, aber es noch keine ausreichende Synergie von hyperspektralen und magnetischen Methoden gibt. Diese Arbeit umfasste drei Fallstudien, bei denen die Drohnengestützte Vermessung von geologischen Zielen in subarktischen bis arktischen Regionen angewendet wurde. Eine Kombination von Drohnen-Technologie mit RGB, Multi- und Hyperspektralkameras und Magnetometern ist vorteilhaft und schuf die Grundlage für eine integrierte Modellierung in den Fallstudien. Die Untersuchungen wurden in einem Gelände mit flacher und zerklüfteter Topografie, verdeckten Zielen und unter oft schlechten Lichtverhältnissen durchgeführt. Unter diesen Bedingungen war es das Ziel, die Anwendbarkeit von Drohnen-basierten Multisensordaten in verschiedenen Explorationsumgebungen zu bewerten. Hochauflösende Oberflächenbilder und Untergrundinformationen aus der Magnetik wurden fusioniert und gemeinsam interpretiert, dabei war eine selektive Gesteinsprobennahme und Analyse ein wesentlicher Bestandteil dieser Arbeit und für die Validierung notwendig. Für eine Eisenerzlagerstätte wurde eine einfache Ressourcenschätzung durchgeführt, indem Magnetik, bildspektroskopisch-basierte Indizes und 2D-Strukturinterpretation integriert wurden. Fotogrammetrische 3D-Modellierung, magnetisches forward-modelling und hyperspektrale Klassifizierungen wurden für eine Karbonatit-Intrusion angewendet, um einen kompletten Explorationsabschnitt zu erfassen. Eine Vektorinversion von magnetischen Daten von Disko Island, Grönland, wurden genutzt, um großräumige 3D-Modelle von undifferenzierten Erdrutschblöcken zu erstellen, sowie diese zu identifizieren und zu vermessen. Die integrierte spektrale und magnetische Kartierung in komplexen Gebieten verbesserte die Erkennungsrate und räumliche Auflösung von Erkundungszielen und reduzierte Zeit, Aufwand und benötigtes Probenmaterial für eine komplexe Interpretation. Der Prototyp einer Multispektralkamera, gebaut für eine Starrflügler-Drohne für die schnelle Vermessung, wurde entwickelt, erfolgreich getestet und zum Teil ausgewertet. Die vorgelegte Arbeit zeigt die Vorteile und Potenziale von Multisensor-Drohnen als praktisches, leichtes, sicheres, schnelles und komfortabel einsetzbares geowissenschaftliches Werkzeug, um digitale Modelle für präzise Rohstofferkundung und geologische Kartierung zu erstellen

Petrology of the Gifford Creek Carbonatite Complex and the Yangibana LREE district, Western Australia: new insights from isotope geochemistry and geochronology

Paul Slezak investigated the Gifford Creek Carbonatite Complex. He determined the complex formed from enriched mantle rocks 1.37 billion years ago and evolved into a rare earth element-bearing mineral deposit. Hastings Technology Metals, which has mining leases in the complex, are using his data for their exploration and mining efforts

IDENTIFICATION OF IRON OXIDES MINERALS IN WESTERN JAHAJPUR REGION, INDIA USING AVIRIS-NG HYPERSPECTRAL REMOTE SENSING

Hyperspectral remote sensing is being considered as an advanced technique for mineral identification of surficial deposits. In this research different iron oxides minerals such as limonite, goethite has been identified using AVIRIS-NG airborne hyperspectral remote sensing covering the Omkarpura, Itwa, and Chhabadiya mines area in Jahajpur Bhilwara, Rajasthan, India. AVIRIS-NG has shown robust performance in iron oxide identification in the study area. Mineral spectral signatures of the AVIRIS-NG data were compared with spectra of USGS spectral library, and field investigated mineral spectra of iron oxides and found very promising. The results allow us to conclude that due the high signal to noise ratios of the AVIRIS-NG, it is capable to identify the different iron bearing minerals in the visible and infrared portion of the electromagnetic spectrum

On the feasibility of imaging carbonatite-hosted rare earth element deposits using remote sensing

© 2016 Gold Open Access: this paper is published under the terms of the CC-BY license. Rare earth elements (REEs) generate characteristic absorption features in visible to shortwave infrared (VNIRSWIR) reflectance spectra. Neodymium (Nd) has among the most prominent absorption features of the REEs and thus represents a key pathfinder element for the REEs as a whole. Given that the world's largest REE deposits are associated with carbonatites, we present spectral, petrographic, and geochemical data from a predominantly carbonatitic suite of rocks that we use to assess the feasibility of imaging REE deposits using remote sensing. Samples were selected to cover a wide range of extents and styles of REE mineralization, and encompass calcio-, ferro-and magnesio-carbonatites. REE ores from the Bayan Obo (China) and Mountain Pass (United States) mines, as well as REE-rich alkaline rocks from the Motzfeldt and Ilímaussaq intrusions in Greenland, were also included in the sample suite. The depth and area of Nd absorption features in spectra collected under laboratory conditions correlate positively with the Nd content of whole-rock samples. The wavelength of Nd absorption features is predominantly independent of sample lithology and mineralogy. Correlations are most reliable for the two absorption features centered at ∼744 and ∼802 nm that can be observed in samples containing as little as ∼1,000 ppm Nd. By convolving laboratory spectra to the spectral response functions of a variety of remote sensing instruments we demonstrate that hyperspectral instruments with capabilities equivalent to the operational Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) and planned Environmental Mapping and Analysis Program (EnMAP) systems have the spectral resolutions necessary to detect Nd absorption features, especially in high-grade samples with economically relevant REE accumulations (Nd> 30,000 ppm). Adding synthetic noise to convolved spectra indicates that correlations between Nd absorption area and whole-rock Nd content only remain robust when spectra have signal-to-noise ratios in excess of ∼250:1. Although atmospheric interferences are modest across the wavelength intervals relevant for Nd detection, most REE-rich outcrops are too small to be detectable using satellite-based platforms with>30-m spatial resolutions. However, our results indicate that Nd absorption features should be identifiable in high-quality, airborne, hyperspectral datasets collected at meter-scale spatial resolutions. Future deployment of hyperspec-tral instruments on unmanned aerial vehicles could enable REE grade to be mapped at the centimeter scale across whole deposits

Geological, Mineralogical and Geochemical Characterisation of the Heavy Rare Earth-rich Carbonatites at Lofdal, Namibia

This study considered the geology, mineralogy, geochemistry, formation and evolution of the heavy rare earth element (HREE) mineralised Lofdal alkaline carbonatite complex (LACC), which is located on the Bergville and Lofdal farms northwest of Khorixas, in the Kunene Region of the Republic of Namibia. . Field methods used included mapping, ground and hyperspectral airborne geophysics, and sampling. Analytical techniques used were optical petrography and CL, XRF, ICP-AES, backscattered and secondary electron imaging, electron microprobe, LA-ICP-MS, leaching, as well as carbon and oxygen stable isotope determination. The LACC comprises a swarm of dykes, mainly calcite carbonatite but also dolomite and ankerite carbonatite dykes (classified into five types) and two newly discovered plugs of calcite carbonatite (‘Main’ and ‘Emanya’), with associated dykes and plugs of phonolites, syenites and rare mafic rocks. These all intrude into the Huab Metamorphic Complex basement rocks within a NE-SW shear zone over 30 km long. The main HREE host mineral is xenotime-(Y). It occurs in highly oxidised iron-rich calcite carbonatite dykes mantling and replacing zircon, associated with hematite, thorite and apatite, or associated with monazite-(Ce), synchysite-(Ce), and parisite-(Ce), replacing the fluorocarbonates; it also forms aggregates in ankerite carbonatite. Although xenotime-(Y) occurs throughout the paragenetic sequence, there is much evidence for hydrothermal fluid activity at Lofdal, altering the dykes, and taking xenotime-(Y) into brecciated carbonate veins in albitised country rock (fenite). Radiogenic (Sr, Nd-Sm, U-Pb) and C and O stable isotope studies confirm that the carbonatite, derived from an enriched mantle, is the source of the REE. Mineralisation was contemporaneous with carbonatite emplacement at 765 ±16 Ma. Magmatic fluids >300°C were diluted with cool meteoric fluids. Abundant fluorite and carbonate indicate roles for F- and CO32- in addition to Cl- in REE transport. These ligands form the most stable complexes with HREE and since xenotime is soluble in concentrated alkali halide solutions, they could have preferentially transported and then deposited xenotime. Many of the features of Lofdal are common to other REE-rich carbonatite complexes but the xenotime-(Y) abundance is so far unique. The high amount of fluid activity in shear zones around the dyke swarm and probably a higher proportion of HREE in the original magmas seem to be the main differentiating features.Ministry of Mines and Energy, Energy Africa PTY Ltd, Africa American Institute and Ministry of Education of Namibi

Research on quantitative inversion of ion adsorption type rare earth ore based on convolutional neural network

Rare earth resource is a national strategic resource, which plays an essential role in the field of high technology research and development. In this paper, we aim to use remote sensing quantitative inversion prospecting technology, use surface-to-surface mode, and model inversion and evaluation through convolutional neural network model to achieve a new research method for large-scale, low-cost, rapid and efficient exploration of ion-adsorbed rare earth ore. The results show that the RE2O3 content of samples has significant negative correlation with the second, third and fourth band of GF-2 image, but has no significant correlation with the first band of GF-2 image; the convolution neural network model can be used to reconstruct the RE2O3 content. The content distribution map of RE2O3 obtained by inversion is similar to that of geochemical map, which indicates that the convolution neural network model can be used to invert the RE2O3 content in the sampling area. The quantitative inversion results show that the content distribution characteristics of ion adsorption rare earth ore in the study area are basically consistent with the actual situation; there are two main high anomaly areas in the study area. The high anomaly area I is a known mining area, and the high anomaly area II can be a prospective area of ion adsorption type rare earth deposit. It shows that the remote sensing quantitative inversion prospecting method of ion adsorption type rare earth deposit based on Convolutional Neural Networks (CNN) model is feasible