Mineral mapping by hyperspectral remote sensing in West Greenland using airborne, ship-based and terrestrial platforms

While multispectral images have been in regular use since the 1970s, the widespread use of hyperspectral images is a relatively recent trend. This technology comprises remote measurement of specific chemical and physical properties of surface materials through imaging spectroscopy. Regional geological mapping and mineral exploration are among the main applications that may benefit from hyperspectral technology. Minerals and rocks exhibit diagnostic spectral features throughout the electromagnetic spectrum that allow their chemical composition and relative abundance to be mapped. Most studies using hyperspectral data for geological applications have concerned areas with arid to semi-arid climates, and using airborne data collection. Other studies have investigated terrestrial outcrop sensing and integration with laser scanning 3D models in ranges of up to a few hundred metres, whereas less attention has been paid to ground-based imaging of more distant targets such as mountain ridges, cliffs or the walls of large pits. Here we investigate the potential of using such data in well-exposed Arctic regions with steep topography as part of regional geological mapping field campaigns, and to test how airborne hyperspectral data can be combined with similar data collected on the ground or from moving platforms such as a small ship. The region between the fjords Ikertoq and Kangerlussuaq (Søndre Strømfjord) in West Greenland was selected for a field study in the summer of 2016. This region is located in the southern part of the Palaeoproterozoic Nagssugtoqidian orogen and consists of high-grade metamorphic ortho- and paragneisses and metabasic rocks (see below). A regional airborne hyperspectral data set (i.e. HyMAP) was acquired here in 2002 (Tukiainen & Thorning 2005), comprising 54 flight lines covering an area of c. 7500 km2; 19 of these flight lines were selected for the present study (Fig. 1). The target areas visited in the field were selected on the basis of preliminary interpretations of HyMap scenes and geology (Korstgård 1979). Two different sensors were utilised to acquire the new hyperspectral data, predominantly a Specim AisaFenix hyperspectral scanner due to its wide spectral range covering the visible to near infrared and shortwave infrared parts of the electromagnetic spectrum. A Rikola Hyperspectral Imager constituted a secondary imaging system. It is much smaller and lighter than the Fenix scanner, but is spectrally limited to the visible near infrared range. The results obtained from combining the airborne hyperspectral data and the Rikola instrument are presented in Salehi (2018), this volume. In addition, representative samples of the main rock types were collected for subsequent laboratory analysis. A parallel study was integrated with geological and 3D photogrammetric mapping in Karrat region farther north in West Greenland (Rosa et al. 2017; Fig. 1).

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Influence of the daylight illumination a weather conditions on Airborne Thermal Infrared Hyperspectral geological mapping

10.5194/egusphere-egu21-7494 ◽

2021 ◽

Author(s):

Stephane Boubanga Tombet ◽

Jean-Philippe Gagnon ◽

Holger Eichstaedt ◽

Joanne Ho

Keyword(s):

Near Infrared ◽

Thermal Infrared ◽

Hyperspectral Data ◽

Geological Mapping ◽

Electromagnetic Spectrum ◽

Spectral Features ◽

Absorption Bands ◽

Mineral Mapping ◽

Atmospheric Compensation ◽

Spectral Ranges

The use of airborne remote sensing techniques for geological mapping offers many benefits as it allows coverage of large areas in a very efficient way. &#160;While hyperspectral imaging from airborne/spaceborne platforms is now a well-established method applied to resolve many geological problems, it has mostly been developed only in the Visible-Near Infrared (VNIR, 0.4&#8211;1.0 mm) and Shortwave Infrared (SWIR, 1.0&#8211;2.5 mm) regions of the electromagnetic spectrum. However, the reflectance spectral features measured in the VNIR and SWIR spectral ranges are generally overtones and combination bands from fundamental absorption bands at longer wavelengths, such as in the Longwave Infrared (LWIR, 8&#8211;12 mm). The single absorption bands in the VNIR and SWIR spectral ranges are often very closely spaced so that the reflectance features measured by common spectrometers in this spectral region are typically broad and/or suffer from strong overlapping, which raises selectivity issues for mineral identification in some cases.The inherent self-emission associated with LWIR under ambient conditions allows airborne mineral mapping in various weather (cloudy, partly cloudy or clear sky) and illumination (day or night) conditions. For this reason, LWIR often refers to the thermal infrared (TIR) spectral range. Solid targets such as minerals not only emit but also reflect TIR radiation. Since the two phenomena occur simultaneously, they end-up mixed in the radiance measured at the sensor level. The spectral features observed in a TIR spectrum of the sky and the atmosphere mostly correspond to ozone, water&#160; vapor, carbon dioxide, methane and nitrous oxide with pretty sharp and narrow features compared with the infrared signature of solid materials such as minerals. The sharp spectral features of atmospheric gases are mixed up with broad minerals features in the collected geological mapping data, to unveil the spectral features associated with minerals from TIR measurements, the respective contributions of self-emission and reflection in the measurement must be &#171;unmixed&#187; and the atmospheric contributions must be compensated. This procedure refers to temperature-emissivity separation (TES). Therefore, to achieve an efficient TES and atmospheric compensation, the collection time and conditions of LWIR airborne hyperspectral data is of importance. Data of a flight mission in Southern Spain collected systematically at different times of the day (morning, mid-day and night) and in different altitudes using the Telops Hyper-Cam airborne system, a passive TIR hyperspectral sensor based on Fourier transform spectroscopy, were analyzed. TES was carried out on the hyperspectral data using two different approaches: a) Telops Reveal FLAASH IR software and b) DIMAP In-scene atmospheric compensation algorithm in order to retrieve thermodynamic temperature map and spectral emissivity data. Spectral analysis of the emissivity data with different mineral mapping methods based on commercial spectral libraries was used to compare results obtained during the different flight times and altitudes using the two post-processing methodologies. The results are discussed in the light of optimizing LWIR-based airborne operations in time and altitude to achieve best results for routine field mineral mapping applications such as in mining, soil science or archaeology, where the spatial analysis of mineral and chemical distribution is essential

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MULTI-LABEL CLASSIFICATION FOR DRILL-CORE HYPERSPECTRAL MINERAL MAPPING

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b3-2020-383-2020 ◽

2020 ◽

Vol XLIII-B3-2020 ◽

pp. 383-388

Author(s):

I. C. Contreras ◽

M. Khodadadzadeh ◽

R. Gloaguen

Keyword(s):

Near Infrared ◽

Short Wave ◽

Hyperspectral Data ◽

Training Data ◽

Electromagnetic Spectrum ◽

Drill Core ◽

Data Set ◽

Mineral Mapping ◽

Mapping Task ◽

Liberation Analysis

Abstract. A multi-label classification concept is introduced for the mineral mapping task in drill-core hyperspectral data analysis. As opposed to traditional classification methods, this approach has the advantage of considering the different mineral mixtures present in each pixel. For the multi-label classification, the well-known Classifier Chain method (CC) is implemented using the Random Forest (RF) algorithm as the base classifier. High-resolution mineralogical data obtained from Scanning Electron Microscopy (SEM) instrument equipped with the Mineral Liberation Analysis (MLA) software are used for generating the training data set. The drill-core hyperspectral data used in this paper cover the visible-near infrared (VNIR) and the short-wave infrared (SWIR) range of the electromagnetic spectrum. The quantitative and qualitative analysis of the obtained results shows that the multi-label classification approach provides meaningful and descriptive mineral maps and outperforms the single-label RF classification for the mineral mapping task.

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Atmospheric Compensation of PRISMA Data by Means of a Learning Based Approach

Remote Sensing ◽

10.3390/rs13152967 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2967

Author(s):

Nicola Acito ◽

Marco Diani ◽

Gregorio Procissi ◽

Giovanni Corsini

Keyword(s):

Spectral Range ◽

Near Infrared ◽

Random Noise ◽

Synthetic Data ◽

Regression Function ◽

Short Wave ◽

Hyperspectral Data ◽

Electromagnetic Spectrum ◽

Great Opportunity ◽

Atmospheric Compensation

Atmospheric compensation (AC) allows the retrieval of the reflectance from the measured at-sensor radiance and is a fundamental and critical task for the quantitative exploitation of hyperspectral data. Recently, a learning-based (LB) approach, named LBAC, has been proposed for the AC of airborne hyperspectral data in the visible and near-infrared (VNIR) spectral range. LBAC makes use of a parametric regression function whose parameters are learned by a strategy based on synthetic data that accounts for (1) a physics-based model for the radiative transfer, (2) the variability of the surface reflectance spectra, and (3) the effects of random noise and spectral miscalibration errors. In this work we extend LBAC with respect to two different aspects: (1) the platform for data acquisition and (2) the spectral range covered by the sensor. Particularly, we propose the extension of LBAC to spaceborne hyperspectral sensors operating in the VNIR and short-wave infrared (SWIR) portion of the electromagnetic spectrum. We specifically refer to the sensor of the PRISMA (PRecursore IperSpettrale della Missione Applicativa) mission, and the recent Earth Observation mission of the Italian Space Agency that offers a great opportunity to improve the knowledge on the scientific and commercial applications of spaceborne hyperspectral data. In addition, we introduce a curve fitting-based procedure for the estimation of column water vapor content of the atmosphere that directly exploits the reflectance data provided by LBAC. Results obtained on four different PRISMA hyperspectral images are presented and discussed.

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Orbital and atmospheric characterization of the planet within the gap of the PDS 70 transition disk

Astronomy and Astrophysics ◽

10.1051/0004-6361/201833584 ◽

2018 ◽

Vol 617 ◽

pp. L2 ◽

Cited By ~ 66

Author(s):

A. Müller ◽

M. Keppler ◽

Th. Henning ◽

M. Samland ◽

G. Chauvin ◽

...

Keyword(s):

Adaptive Optics ◽

Near Infrared ◽

Direct Detection ◽

Spectral Energy Distribution ◽

Spectral Energy ◽

Infrared Range ◽

Data Set ◽

Cloud Properties ◽

Spectrophotometric Data ◽

First Time

Context. The observation of planets in their formation stage is a crucial but very challenging step in understanding when, how, and where planets form. PDS 70 is a young pre-main sequence star surrounded by a transition disk, in the gap of which a planetary-mass companion has recently been discovered. This discovery represents the first robust direct detection of such a young planet, possibly still at the stage of formation. Aims. We aim to characterize the orbital and atmospheric properties of PDS 70 b, which was first identified on May 2015 in the course of the SHINE survey with SPHERE, the extreme adaptive-optics instrument at the VLT. Methods. We obtained new deep SPHERE/IRDIS imaging and SPHERE/IFS spectroscopic observations of PDS 70 b. The astrometric baseline now covers 6 yr, which allowed us to perform an orbital analysis. For the first time, we present spectrophotometry of the young planet which covers almost the entire near-infrared range (0.96–3.8 μm). We use different atmospheric models covering a large parameter space in temperature, log g, chemical composition, and cloud properties to characterize the properties of the atmosphere of PDS 70 b. Results. PDS 70 b is most likely orbiting the star on a circular and disk coplanar orbit at ~22 au inside the gap of the disk. We find a range of models that can describe the spectrophotometric data reasonably well in the temperature range 1000–1600 K and log g no larger than 3.5 dex. The planet radius covers a relatively large range between 1.4 and 3.7 RJ with the larger radii being higher than expected from planet evolution models for the age of the planet of 5.4 Myr. Conclusions. This study provides a comprehensive data set on the orbital motion of PDS 70 b, indicating a circular orbit and a motion coplanar with the disk. The first detailed spectral energy distribution of PDS 70 b indicates a temperature typical of young giant planets. The detailed atmospheric analysis indicates that a circumplanetary disk may contribute to the total planetflux.

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Hylite: a hyperspectral toolbox for open pit mapping

10.5194/egusphere-egu2020-13563 ◽

2020 ◽

Author(s):

Sam Thiele ◽

Sandra Lorenz ◽

Moritz Kirsch ◽

Richard Gloaguen

Keyword(s):

Short Wave ◽

Mineral Deposit ◽

Hyperspectral Data ◽

Geological Mapping ◽

Open Pit ◽

Electromagnetic Spectrum ◽

Mining Operations ◽

Open Pit Mines ◽

Volcanogenic Massive Sulphide ◽

Potential Applications

Hyperspectral imaging is a powerful tool for mapping mineralogy and lithology in core and outcrops, as many minerals show distinct spectral features in the commonly analysed visible, near, short-wave and long-wave infrared regions of the electromagnetic spectrum. High resolution ground and UAS (unmanned aerial system)-based sensors thus have significant potential as a tool for rapid and non-invasive geological mapping in mining operations, exploration campaigns and scientific research. However, the geometrical complexity of many outcrops (e.g. cliffs, open-pit mines) can result in significant technical challenges when acquiring and processing hyperspectral data. In this contribution we present updates to the previously published MEPHySTo python toolbox for correcting, georeferencing, projecting and analysing geometrically complex hyperspectral scenes. We showcase these methods using datasets covering volcanogenic massive sulphide (VMS) mineralisation exposed within open pit mines in Rio Tinto (Spain), and interpret possible structural and lithological controls on mineralization. Potential applications of hyperspectral mapping for grade control, outcrop mapping and the characterisation of different mineral deposit styles are also discussed.

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New evaluation method to detect physiological stress in fruit trees by airborne hyperspectral image spectroscopy

International Journal of Horticultural Science ◽

10.31421/ijhs/16/1/860 ◽

2010 ◽

Vol 16 (1) ◽

Author(s):

J. Tamás

Keyword(s):

Precision Agriculture ◽

Near Infrared ◽

Evaluation Method ◽

Hyperspectral Image ◽

Physiological Stress ◽

Incident Light ◽

Hyperspectral Data ◽

Data Cube ◽

Infrared Range ◽

Image Spectroscopy

Nowadays airborne remote sensing data are increasingly used in precision agriculture. The fast space-time dependent localization of stresses in orchards, which allows for a more efficient application of horticultural technologies, could lead to improved sustainable precise management. The disadvantage of the near field multi and hyper spectroscopy is the spot sample taking, which can apply independently only for experimental survey in plantations. The traditional satellite images is optionally suitable for precision investigation because of the low spectral and ground resolution on field condition. The presented airborne hyperspectral image spectroscopy reduces above mentioned disadvantages and at the same time provides newer analyzing possibility to the user. In this paper we demonstrate the conditions of data base collection and some informative examination possibility. The estimating of the board band vegetation indices calculated from reflectance is well known in practice of the biomass stress examinations. In this method the N-dimension spectral data cube enables to calculate numerous special narrow band indexes and to evaluate maps. This paper aims at investigating the applied hyperspectral analysis for fruit tree stress detection. In our study, hyperspectral data were collected by an AISADUAL hyperspectral image spectroscopy system, with high (0,5-1,5 m) ground resolution. The research focused on determining of leaves condition in different fruit plantations in the peach orchard near Siófok. Moreover the spectral reflectance analyses could provide more information about plant condition due to changes in the absorption of incident light in the visible and near infrared range of the spectrum.

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A supervised technique for drill-core mineral mapping using Hyperspectral data

10.5194/egusphere-egu2020-13526 ◽

2020 ◽

Author(s):

Cecilia Contreras ◽

Mahdi Khodadadzadeh ◽

Laura Tusa ◽

Richard Gloaguen

Keyword(s):

Machine Learning ◽

Near Infrared ◽

Hyperspectral Data ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Drill Core ◽

Data Set ◽

Mineral Mapping ◽

Core Logging ◽

Learning Techniques

Drilling is a key task in exploration campaigns to characterize mineral deposits at depth. Drillcores are first logged in the field by a geologist and with regards to, e.g., mineral assemblages, alteration patterns, and structural features. The core-logging information is then used to locate and target the important ore accumulations and select representative samples that are further analyzed by laboratory measurements (e.g., Scanning Electron Microscopy (SEM), Xray diffraction (XRD), X-ray Fluorescence (XRF)). However, core-logging is a laborious task and subject to the expertise of the geologist. Hyperspectral imaging is a non-invasive and non-destructive technique that is increasingly being used to support the geologist in the analysis of drill-core samples. Nonetheless, the benefit and impact of using hyperspectral data depend on the applied methods. With this in mind, machine learning techniques, which have been applied in different research fields, provide useful tools for an advance and more automatic analysis of the data. Lately, machine learning frameworks are also being implemented for mapping minerals in drill-core hyperspectral data. In this context, this work follows an approach to map minerals on drill-core hyperspectral data using supervised machine learning techniques, in which SEM data, integrated with the mineral liberation analysis (MLA) software, are used in training a classifier. More specifically, the highresolution mineralogical data obtained by SEM-MLA analysis is resampled and co-registered to the hyperspectral data to generate a training set. Due to the large difference in spatial resolution between the SEM-MLA and hyperspectral images, a pre-labeling strategy is required to link these two images at the hyperspectral data spatial resolution. In this study, we use the SEM-MLA image to compute the abundances of minerals for each hyperspectral pixel in the corresponding SEM-MLA region. We then use the abundances as features in a clustering procedure to generate the training labels. In the final step, the generated training set is fed into a supervised classification technique for the mineral mapping over a large area of a drill-core. The experiments are carried out on a visible to near-infrared (VNIR) and shortwave infrared (SWIR) hyperspectral data set and based on preliminary tests the mineral mapping task improves significantly.

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Wine aging authentication through Near Infrared Spectroscopy: a feasibility study on chips and barrel aged wines

OENO One ◽

10.20870/oeno-one.2020.54.1.2921 ◽

2020 ◽

Vol 54 (1) ◽

pp. 165-173

Author(s):

Tiziana Nardi ◽

Maurizio Petrozziello ◽

Raffaele Girotto ◽

Michele Fugaro ◽

Raffaele Antonio Mazzei ◽

...

Keyword(s):

Data Analysis ◽

Least Squares ◽

Partial Least Squares ◽

Aging Process ◽

Near Infrared ◽

Research Centre ◽

Electromagnetic Spectrum ◽

Method Performance ◽

Data Set ◽

Wine Aging

Aim: This research primarily focuses on exploring the suitability of near infrared (NIR) spectroscopy with multivariate data analysis as a tool to classify commercial wines depending on the aging process. It is aimed at discriminating between wines aged in barrels and those obtained using alternative products (chips).Methods and Results: Around 75 commercial barrel-aged red wines issued from the appellation “Valpolicella” (Italy) were analyzed. Moreover, 15 wines were aged at the experimental winery of the Research Centre of Viticulture and Enology in Asti using different types of commercial oak chips. Wines were analyzed in transmittance using NIR regions of the electromagnetic spectrum. Principal component analysis (PCA) and partial least squares (PLS) analyses were used to classify wines: a preliminary step was carried out using PCA that showed interesting groups in the whole data set. Next, in order to test if combined explanatory variables made it possible to discriminate treatments and how they are useful to predict which group a new observation will belong to, an orthogonal partial least squares discriminant analysis (OPLS-DA) was carried out. Several wine groups were considered, defined by factors including the aging process, the type of oak used for aging (wood barriques, barrels or chips) and the wine typologies (differing for some enological parameters).Conclusions: Overall, OPLS-DA models correctly classified >90 % of the wines. These results demonstrate the potential of combining spectroscopy with chemometric data analysis as a rapid method to classify wines according to their aging process. Nevertheless, the development of a mathematical model for predictive purposes is a complex task: indeed, large databases for different wines should be constructed, and other spectral IR zones might be evaluated for improving the method performance in determining wine aging process.Significance and impact of the study: This study contributes to the development of an easy-to-use and easily applicable NIR method for correlating the infrared “fingerprint” spectrum with the aging process in wines, aimed at implementing a technique able to discriminate wines aged with different wood types, that can be progressively used in the laboratory for routine fraud inspection.

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Lithology Classification Using TASI Thermal Infrared Hyperspectral Data with Convolutional Neural Networks

Remote Sensing ◽

10.3390/rs13163117 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3117

Author(s):

Huize Liu ◽

Ke Wu ◽

Honggen Xu ◽

Ying Xu

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Near Infrared ◽

Three Dimensional ◽

Thermal Infrared ◽

Hyperspectral Data ◽

Gansu Province ◽

Electromagnetic Spectrum ◽

Lithological Mapping ◽

Classification Feature

In recent decades, lithological mapping techniques using hyperspectral remotely sensed imagery have developed rapidly. The processing chains using visible-near infrared (VNIR) and shortwave infrared (SWIR) hyperspectral data are proven to be available in practice. The thermal infrared (TIR) portion of the electromagnetic spectrum has considerable potential for mineral and lithology mapping. In particular, the abovementioned rocks at wavelengths of 8–12 μm were found to be discriminative, which can be seen as a characteristic to apply to lithology classification. Moreover, it was found that most of the lithology mapping and classification for hyperspectral thermal infrared data are still carried out by traditional spectral matching methods, which are not very reliable due to the complex diversity of geological lithology. In recent years, deep learning has made great achievements in hyperspectral imagery classification feature extraction. It usually captures abstract features through a multilayer network, especially convolutional neural networks (CNNs), which have received more attention due to their unique advantages. Hence, in this paper, lithology classification with CNNs was tested on thermal infrared hyperspectral data using a Thermal Airborne Spectrographic Imager (TASI) at three small sites in Liuyuan, Gansu Province, China. Three different CNN algorithms, including one-dimensional CNN (1-D CNN), two-dimensional CNN (2-D CNN) and three-dimensional CNN (3-D CNN), were implemented and compared to the six relevant state-of-the-art methods. At the three sites, the maximum overall accuracy (OA) based on CNNs was 94.70%, 96.47% and 98.56%, representing improvements of 22.58%, 25.93% and 16.88% over the worst OA. Meanwhile, the average accuracy of all classes (AA) and kappa coefficient (kappa) value were consistent with the OA, which confirmed that the focal method effectively improved accuracy and outperformed other methods.

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3D modelling of a mineral deposit using drill core hyperspectral data

10.5194/egusphere-egu2020-14952 ◽

2020 ◽

Author(s):

Roberto De La Rosa ◽

Mahdi Khodadadzadeh ◽

Cecilia Contreras ◽

Laura Tusa ◽

Moritz Kirsch ◽

...

Keyword(s):

Categorical Data ◽

Near Infrared ◽

Mining Industry ◽

Structural Features ◽

Derived Categories ◽

Hyperspectral Data ◽

Drill Core ◽

Data Set ◽

Core Samples ◽

Core Logging

Drill core samples have been traditionally used by the mining industry to make resource estimations and to build geological models. The hyperspectral drill core scanning has become a popular tool in mineral exploration because it provides a non-destructive method to rapidly characterise structural features, alteration patterns and rock mineralogy in a cost effective way. Typically, the hyperspectral sensors cover a wide spectral range from visible and near-infrared (VNIR) to short and long wave infrared (SWIR and LWIR). The spectral features in this range will help to characterize a large number of mineral phases and complement the traditional core logging techniques. The hyperspectral core scanning provide mineralogical information in a millimetre scale for the entire borehole, which fills the gap between the microscopic scale of some of the laboratory analytical methods or the sparse chemical assays and the meter scale from the lithological descriptions.However, applying this technique to the core samples of an entire ore deposit results in big datasets. Therefore, there is the need of a workflow to build a 3D geological model conditioned by the data applying suitable data reduction methods and appropriate interpolation techniques.This contribution presents a case study in the combination of traditional core logging and hyperspectral core logging for geological modelling. To attain mineral and alteration maps from the hyperspectral data, unsupervised classification techniques were applied generating a categorical data set. The amount of data was reduced by the application of a domain generation algorithm based on the hyperspectral information. The domain generated by the algorithm is a compositional categorical data set that was then fed to condition the application of stochastic Plurigaussian simulations in the construction of 3D models of geological domains. This technique allows to simulate the spatial distribution of the hyperspectral derived categories, to make a resource estimation and to calculate its associated uncertainty.

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