Automated lithological mapping using airborne hyperspectral thermal infrared data: A case study from Anchorage Island, Antarctica

The thermal infrared portion of the electromagnetic spectrum has considerable potential for mineral and lithological mapping of the most abundant rock-forming silicates that do not display diagnostic features at visible and shortwave infrared wavelengths. Lithological mapping using visible and shortwave infrared hyperspectral data is well developed and established processing chains are available, however there is a paucity of such methodologies for hyperspectral thermal infrared data. Here we present a new fully automated processing chain for deriving lithological maps from hyperspectral thermal infrared data and test its applicability using the first ever airborne hyperspectral thermal data collected in the Antarctic. A combined airborne hyperspectral survey, targeted geological field mapping campaign and detailed mineralogical and geochemical datasets are applied to small test site in West Antarctica where the geological relationships are representative of continental margin arcs. The challenging environmental conditions and cold temperatures in the Antarctic meant that the data have a significantly lower signal to noise ratio than is usually attained from airborne hyperspectral sensors. We applied preprocessing techniques to improve the signal to noise ratio and convert the radiance images to ground leaving emissivity. Following preprocessing we developed and applied a fully automated processing chain to the hyperspectral imagery, which consists of the following six steps: (1) superpixel segmentation, (2) determine the number of endmembers, (3) extract endmembers from superpixels, (4) apply fully constrained linear unmixing, (5) generate a predictive classification map, and (6) automatically label the predictive classes to generate a lithological map. The results show that the image processing chain was successful, despite the low signal to noise ratio of the imagery; reconstruction of the hyperspectral image from the endmembers and their fractional abundances yielded a root mean square error of 0.58%. The results are encouraging with the thermal imagery allowing clear distinction between granitoid types. However, the distinction of fine grained, intermediate composition dykes is not possible due to the close geochemical similarity with the country rock

Mineral identification using data-mining in hyperspectral infrared imagery

Les applications de l’imagerie infrarouge dans le domaine de la géologie sont principalement des applications hyperspectrales. Elles permettent entre autre l’identification minérale, la cartographie, ainsi que l’estimation de la portée. Le plus souvent, ces acquisitions sont réalisées in-situ soit à l’aide de capteurs aéroportés, soit à l’aide de dispositifs portatifs. La découverte de minéraux indicateurs a permis d’améliorer grandement l’exploration minérale. Ceci est en partie dû à l’utilisation d’instruments portatifs. Dans ce contexte le développement de systèmes automatisés permettrait d’augmenter à la fois la qualité de l’exploration et la précision de la détection des indicateurs. C’est dans ce cadre que s’inscrit le travail mené dans ce doctorat. Le sujet consistait en l’utilisation de méthodes d’apprentissage automatique appliquées à l’analyse (au traitement) d’images hyperspectrales prises dans les longueurs d’onde infrarouge. L’objectif recherché étant l’identification de grains minéraux de petites tailles utilisés comme indicateurs minéral -ogiques. Une application potentielle de cette recherche serait le développement d’un outil logiciel d’assistance pour l’analyse des échantillons lors de l’exploration minérale. Les expériences ont été menées en laboratoire dans la gamme relative à l’infrarouge thermique (Long Wave InfraRed, LWIR) de 7.7m à 11.8 m. Ces essais ont permis de proposer une méthode pour calculer l’annulation du continuum. La méthode utilisée lors de ces essais utilise la factorisation matricielle non négative (NMF). En utlisant une factorisation du premier ordre on peut déduire le rayonnement de pénétration, lequel peut ensuite être comparé et analysé par rapport à d’autres méthodes plus communes. L’analyse des résultats spectraux en comparaison avec plusieurs bibliothèques existantes de données a permis de mettre en évidence la suppression du continuum. Les expérience ayant menés à ce résultat ont été conduites en utilisant une plaque Infragold ainsi qu’un objectif macro LWIR. L’identification automatique de grains de différents matériaux tels que la pyrope, l’olivine et le quartz a commencé. Lors d’une phase de comparaison entre des approches supervisées et non supervisées, cette dernière s’est montrée plus approprié en raison du comportement indépendant par rapport à l’étape d’entraînement. Afin de confirmer la qualité de ces résultats quatre expériences ont été menées. Lors d’une première expérience deux algorithmes ont été évalués pour application de regroupements en utilisant l’approche FCC (False Colour Composite). Cet essai a permis d’observer une vitesse de convergence, jusqu’a vingt fois plus rapide, ainsi qu’une efficacité significativement accrue concernant l’identification en comparaison des résultats de la littérature. Cependant des essais effectués sur des données LWIR ont montré un manque de prédiction de la surface du grain lorsque les grains étaient irréguliers avec présence d’agrégats minéraux. La seconde expérience a consisté, en une analyse quantitaive comparative entre deux bases de données de Ground Truth (GT), nommée rigid-GT et observed-GT (rigide-GT: étiquet manuel de la région, observée-GT:étiquetage manuel les pixels). La précision des résultats était 1.5 fois meilleur lorsque l’on a utlisé la base de données observed-GT que rigid-GT. Pour les deux dernières epxérience, des données venant d’un MEB (Microscope Électronique à Balayage) ainsi que d’un microscopie à fluorescence (XRF) ont été ajoutées. Ces données ont permis d’introduire des informations relatives tant aux agrégats minéraux qu’à la surface des grains. Les résultats ont été comparés par des techniques d’identification automatique des minéraux, utilisant ArcGIS. Cette dernière a montré une performance prometteuse quand à l’identification automatique et à aussi été utilisée pour la GT de validation. Dans l’ensemble, les quatre méthodes de cette thèse représentent des méthodologies bénéfiques pour l’identification des minéraux. Ces méthodes présentent l’avantage d’être non-destructives, relativement précises et d’avoir un faible coût en temps calcul ce qui pourrait les qualifier pour être utilisée dans des conditions de laboratoire ou sur le terrain.The geological applications of hyperspectral infrared imagery mainly consist in mineral identification, mapping, airborne or portable instruments, and core logging. Finding the mineral indicators offer considerable benefits in terms of mineralogy and mineral exploration which usually involves application of portable instrument and core logging. Moreover, faster and more mechanized systems development increases the precision of identifying mineral indicators and avoid any possible mis-classification. Therefore, the objective of this thesis was to create a tool to using hyperspectral infrared imagery and process the data through image analysis and machine learning methods to identify small size mineral grains used as mineral indicators. This system would be applied for different circumstances to provide an assistant for geological analysis and mineralogy exploration. The experiments were conducted in laboratory conditions in the long-wave infrared (7.7μm to 11.8μm - LWIR), with a LWIR-macro lens (to improve spatial resolution), an Infragold plate, and a heating source. The process began with a method to calculate the continuum removal. The approach is the application of Non-negative Matrix Factorization (NMF) to extract Rank-1 NMF and estimate the down-welling radiance and then compare it with other conventional methods. The results indicate successful suppression of the continuum from the spectra and enable the spectra to be compared with spectral libraries. Afterwards, to have an automated system, supervised and unsupervised approaches have been tested for identification of pyrope, olivine and quartz grains. The results indicated that the unsupervised approach was more suitable due to independent behavior against training stage. Once these results obtained, two algorithms were tested to create False Color Composites (FCC) applying a clustering approach. The results of this comparison indicate significant computational efficiency (more than 20 times faster) and promising performance for mineral identification. Finally, the reliability of the automated LWIR hyperspectral infrared mineral identification has been tested and the difficulty for identification of the irregular grain’s surface along with the mineral aggregates has been verified. The results were compared to two different Ground Truth(GT) (i.e. rigid-GT and observed-GT) for quantitative calculation. Observed-GT increased the accuracy up to 1.5 times than rigid-GT. The samples were also examined by Micro X-ray Fluorescence (XRF) and Scanning Electron Microscope (SEM) in order to retrieve information for the mineral aggregates and the grain’s surface (biotite, epidote, goethite, diopside, smithsonite, tourmaline, kyanite, scheelite, pyrope, olivine, and quartz). The results of XRF imagery compared with automatic mineral identification techniques, using ArcGIS, and represented a promising performance for automatic identification and have been used for GT validation. In overall, the four methods (i.e. 1.Continuum removal methods; 2. Classification or clustering methods for mineral identification; 3. Two algorithms for clustering of mineral spectra; 4. Reliability verification) in this thesis represent beneficial methodologies to identify minerals. These methods have the advantages to be a non-destructive, relatively accurate and have low computational complexity that might be used to identify and assess mineral grains in the laboratory conditions or in the field

Multispectral and Hyperspectral Remote Sensing Data for Mineral Exploration and Environmental Monitoring of Mined Areas

In recent decades, remote sensing technology has been incorporated in numerous mineral exploration projects in metallogenic provinces around the world. Multispectral and hyperspectral sensors play a significant role in affording unique data for mineral exploration and environmental hazard monitoring. This book covers the advances of remote sensing data processing algorithms in mineral exploration, and the technology can be used in monitoring and decision-making in relation to environmental mining hazard. This book presents state-of-the-art approaches on recent remote sensing and GIS-based mineral prospectivity modeling, offering excellent information to professional earth scientists, researchers, mineral exploration communities and mining companies

Integration of advanced remote sensing and geospatial methodologies to enhance mineral exploration: An example from the southern Gawler Ranges, South Australia

The world’s demand for metals is increasing and there is a growing need for mineral explorers to locate new ore deposits. Globally, discovery of economic mineral deposits is becoming more challenging due to the increasing depths where exploration is being conducted to discover mineral deposits. Most surficial deposits have been discovered, driving exploration into terrains with substantial weathered regolith cover, and requiring new exploration methods. Current traditional exploration methods including geophysics, high density soil sampling and geochemical analysis can be expensive, time consuming and limited in geographic extent. Although remote sensing methods have been applied to regional-scale mineral exploration, there is potential for them to be used more fully in regions where regolith is a continuing challenge. The overarching aim of this multidisciplinary thesis is to develop methods that integrate forms of remote sensing and geospatial information to reduce the risk and cost of exploration in weathered terrains by identifying and mapping surface alteration related to buried mineralisation. The study area used to develop and test these methodologies was the southern Gawler Ranges, South Australia, a region prospective for gold, porphyry-copper and epithermal-silver mineralisation. This semi-arid environment is moderately vegetated with limited geological exposures. Most basement rocks are overlain by approximately 100 m of weathered cover materials presenting challenges for both exploration and remote sensing methods. The broad research aim was addressed through three more specific objectives: 1. Development of an objective regolith-landform map using geospatial data and a repeatable methodology that can be used to guide the early stages of exploration potential assessment; 2. Characterisation of surface expressions of alteration mineralogy and interpretation of landscape processes using airborne hyperspectral imagery and mineralogical data; and 3. Integration of surface geochemistry, mineralogy and regolith-landform mapping to understand and map surface signatures of potential buried mineralisation. An unsupervised classification was applied to geospatial data layers including a Digital Elevation Model, Topographic Position Index and potassium, thorium and uranium gamma-ray radiometrics. This was clustered to generate an objective regolith-landform map representing the main regolith-landform types. This map captured many of the features typically mapped by traditional regolith-landform mapping as assessed by a statistical goodness of fit measure. While not a replacement for the resource-intensive traditional regolith maps derived from extensive field work, this method used freely available geospatial data an objective, repeatable methodology to produce a map that has potential to increase understanding of the landscape and assist targeting of areas of alteration and mineralisation for more detailed exploration. Airborne hyperspectral imagery was analysed by Spectral Feature Fitting, matching image spectra to reference spectra to identify alteration mineralogy. X-ray diffraction was used to independently validate mineralogy present in the landscape providing insight into unclear spatial distributions of some minerals and confirming the presence of key alteration minerals. Landscape processes were interpreted by integrating the spatial distribution of minerals with the objective regolith-landform map. Advanced argillic and argillic alteration were identified in the study area, focused around an exposed alunite breccia at Nankivel Hill. The results placed the central topographic feature, Nankivel Hill, proximal to potential porphyry mineralisation, with Peterlumbo Hill distal to mineralisation as possible chloritic alteration expressed at the surface in this region. Definition of lithologies from major element geochemistry identified ten rock and cover sequence types within the study area. A region-specific pathfinder element suite was defined using interpretation and thresholds of the Nankivel and Peterlumbo Hill rock exposures. The mineral hosts of these pathfinder elements were proposed from interpretation of semi-quantitative X-ray diffraction to determine the influence of weathering on dispersion of pathfinder elements from rock exposure to cover sequence materials. This suggested that most pathfinder elements were hosted in a variety of minerals including alunite, jarosite, microcline, muscovite, orthoclase and hematite in rock exposures and a broader range of feldspars, clays, micas, carbonates and iron oxides associated with cover sequence materials. Definitions of proximal and distal geochemical and mineralogical footprints of a porphyry deposit were delineated using the surface geochemistry, X-ray diffraction and hyperspectral mineralogical data. The landscape position of pathfinder elements was interpreted to recommend sample media with the most potential for identification of pathfinders at higher concentrations. The outcomes of this research demonstrate several encouraging approaches for use of land surface remote sensing and geospatial analysis in the context of mineral exploration in highly weathered and covered terrains. These methods can be integrated easily with more traditional methods and data to improve mineral exploration outcomes for the industry. The increasing need to explore terrains with extensive depths of cover in order to discover new ore deposits suggests that the industry would benefit from integrating these tools to enhance future exploration.Thesis (Ph.D.) -- University of Adelaide, School of Biological Sciences, 202

Caracterização e estudo comparativo de exsudações de hidrocarbonetos e plays petrolíferos em bacias terrestres das regiões central do Irã e sudeste do Brasil usando sensoriamento remoto espectral

Orientador: Carlos Roberto de Souza FilhoTese (doutorado) - Universidade Estadual de Campinas, Instituto de GeociênciasResumo: O objetivo desta pesquisa foi explorar as assinaturas de exsudações de hidrocarbonetos na superfície usando a tecnologia de detecção remota espectral. Isso foi alcançado primeiro, realizando uma revisão abrangente das capacidades e potenciais técnicas de detecção direta e indireta. Em seguida, a técnica foi aplicada para investigar dois locais de teste localizados no Irã e no Brasil, conhecidos por hospedar sistemas ativos de micro-exsudações e afloramentos betuminosos, respectivamente. A primeira área de estudo está localizada perto da cidade de Qom (Irã), e está inserida no campo petrolífero Alborz, enterrado sob sedimentos datados do Oligoceno da Formação Upper Red. O segundo local está localizado perto da cidade de Anhembi (SP), na margem oriental da bacia do Paraná, no Brasil, e inclui acumulações de betume em arenitos triássicos da Formação Pirambóia. O trabalho na área de Qom integrou evidências de (i) estudos petrográficos e geoquímicos em laboratório, (ii) investigações de afloramentos em campo, e (iii) mapeamento de anomalia em larga escala através de conjuntos de dados multi-espectrais ASTER e Sentinel-2. O resultado deste estudo se trata de novos indicadores mineralógicos e geoquímicos para a exploração de micro-exsudações e um modelo de micro-exsudações atualizado. Durante este trabalho, conseguimos desenvolver novas metodologias para análise de dados espectroscópicos. Através da utilização de dados simulados, indicamos que o instrumento de satélite WorldView-3 tem potencial para detecção direta de hidrocarbonetos. Na sequência do estudo, dados reais sobre afloramentos de arenitos e óleo na área de Anhembi foram investigados. A área foi fotografada novamente no chão e usando o sistema de imagem hiperespectral AisaFENIX. Seguiu-se estudos e amostragem no campo,incluindo espectroscopia de alcance fechado das amostras no laboratório usando instrumentos de imagem (ou seja, sisuCHEMA) e não-imagem (ou seja, FieldSpec-4). O estudo demonstrou que uma abordagem espectroscópica multi-escala poderia fornecer uma imagem completa das variações no conteúdo e composição do betume e minerais de alteração que acompanham. A assinatura de hidrocarbonetos, especialmente a centrada em 2300 nm, mostrou-se consistente e comparável entre as escalas e capaz de estimar o teor de betume de areias de petróleo em todas as escalas de imagemAbstract: The objective of this research was to explore for the signatures of seeping hydrocarbons on the surface using spectral remote sensing technology. It was achieved firstly by conducting a comprehensive review of the capacities and potentials of the technique for direct and indirect seepage detection. Next, the technique was applied to investigate two distinctive test sites located in Iran and Brazil known to retain active microseepage systems and bituminous outcrops, respectively. The first study area is located near the city of Qom in Iran, and consists of Alborz oilfield buried under Oligocene sediments of the Upper-Red Formation. The second site is located near the town of Anhembi on the eastern edge of the Paraná Basin in Brazil and includes bitumen accumulations in the Triassic sandstones of the Pirambóia Formation. Our work in Qom area integrated evidence from (i) petrographic, spectroscopic, and geochemical studies in the laboratory, (ii) outcrop investigations in the field, and (iii) broad-scale anomaly mapping via orbital remote sensing data. The outcomes of this study was novel mineralogical and geochemical indicators for microseepage characterization and a classification scheme for the microseepage-induced alterations. Our study indicated that active microseepage systems occur in large parts of the lithofacies in Qom area, implying that the extent of the petroleum reservoir is much larger than previously thought. During this work, we also developed new methodologies for spectroscopic data analysis and processing. On the other side, by using simulated data, we indicated that WorldView-3 satellite instrument has the potential for direct hydrocarbon detection. Following this demonstration, real datasets were acquired over oil-sand outcrops of the Anhembi area. The area was further imaged on the ground and from the air by using an AisaFENIX hyperspectral imaging system. This was followed by outcrop studies and sampling in the field and close-range spectroscopy in the laboratory using both imaging (i.e. sisuCHEMA) and nonimaging instruments. The study demonstrated that a multi-scale spectroscopic approach could provide a complete picture of the variations in the content and composition of bitumen and associated alteration mineralogy. The oil signature, especially the one centered at 2300 nm, was shown to be consistent and comparable among scales, and capable of estimating the bitumen content of oil-sands at all imaging scalesDoutoradoGeologia e Recursos NaturaisDoutor em Geociências2015/06663-7FAPES

AEOLIAN SYSTEM DYNAMICS DERIVED FROM THERMAL INFRARED DATA

Thermal infrared (TIR) remote-sensing and field-based observations were used to study aeolian systems, specifically sand transport pathways, dust emission sources and Saharan atmospheric dust. A method was developed for generating seamless and radiometrically accurate mosaics of thermal infrared data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument. Using a combination of high resolution thermal emission spectroscopy results of sand samples and mosaic satellite data, surface emissivity was derived to map surface composition, which led to improvement in the understanding of sand accumulation in the Gran Desierto of northern Sonora, Mexico. These methods were also used to map sand transport pathways in the Sahara Desert, where the interaction between sand saltation and dust emission sources was explored. The characteristics and dynamics of dust sources were studied at White Sands, NM and in the Sahara Desert. At White Sands, an application was developed for studying the response of dust sources to surface soil moisture based on the relationship between soil moisture, apparent thermal inertia and the erosion potential of dust sources. The dynamics of dust sources and the interaction with sand transport pathways were also studied, focusing on the Bodele Depression of Chad and large dust sources in Mali and Mauritania. A dust detection algorithm was developed using ASTER data, and the spectral emissivity of observed atmospheric dust was related to the dust source area in the Sahara. At the Atmospheric Observatory (IZO) in Tenerife, Spain where direct measurement of the Saharan Air Layer could be made, the cycle of dust events occurring in July 2009 were examined. From the observation tower at the IZO, measurements of emitted longwave atmospheric radiance in the TIR wavelength region were made using a Forward Looking Infrared Radiometer (FLIR) handheld camera. The use of the FLIR to study atmospheric dust from the Saharan is a new application. Supporting data from AERONET and other orbital data enabled study of net radiative forcing

Spectral enhancement of sebass hyperspectral data and its application in mapping of ultramafic rocks

Airborne SEBASS data (measuring hyperspectral thermal emissivity) was acquired and analyzed to map the spatial distribution of ultramafic rocks in remote areas of the Cape Smith greenstone Belt in Nunavik, Northern Quebec, Canada. Spectral analysis in the wavelet domain was developed to address technical issues commonly encountered in these data, namely noise reduction and minimization of atmospheric and surface temperature effects. Mapping of rock units was conducted using the spectral angle mapper applied to image endmembers extracted during wavelet analysis. The endmembers largely represent a suite of minerals validated with X-Ray Diffraction analysis and spectral measurement of rock samples