Multi-scale, multi-sensor data integration for automated 3-D geological mapping

Thiele, Samuel T.; Lorenz, Sandra; Kirsch, Moritz; Acosta, Isabel Cecilia Contreras; Tuşa, Laura; Herrmann, Erik; Möckel, Robert; Gloaguen, Richard

doi:10.1016/j.oregeorev.2021.104252

Cited by 56 publications

(60 citation statements)

References 43 publications

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“…An initial concern in this study was the fact that the used imaging hyperspectral systems were not covering the spectral range 2000-2500 nm, which is diagnostic for many geological materials (see, e.g., [4,9]). Imaging systems covering a wider spectral range have been used to acquire panoramic images in open pit mines [21][22][23][24]29,[74][75][76]. To mention the most recent, Barton et al [75] mapped different mixtures of carbonates, mica-rich muscovite mica, kaolinite, and gypsum in highwalls and outcrops using a system with 640 bands that integrates two sensors to cover from 400 to 2500 nm.…”

Section: Discussionmentioning

confidence: 99%

“…To mention the most recent, Barton et al [75] mapped different mixtures of carbonates, mica-rich muscovite mica, kaolinite, and gypsum in highwalls and outcrops using a system with 640 bands that integrates two sensors to cover from 400 to 2500 nm. Thiele et al [76] used an equivalent (albeit much heavier) system to map an open-pit mine face in terms of oxidized materials, massive sulfides, Mg-and Fe-rich chlorite, two sericitic units, and shales. Nevertheless, our laboratory results very clearly indicated the strong discriminant power of the spectral ranges and signal quality of the much affordable FX10 and FX17 systems for the particular minerals of interest in this case of study.…”

Section: Discussionmentioning

confidence: 99%

“…Spatial resolution can also be improved by the co-registration of hyperspectral images to conventional digital photography ("RGB") and subsequent image fusion. Actually, a crude co-registration using 2D coordinates only was used in this study as an aid for photointerpretation, but the correct co-registration requires 3D coordinates and, thus, generating a digital surface model [30,31,76], an involved task that will be worth addressing in the forthcoming study of the actual mine excavation face.…”

Section: Discussionmentioning

confidence: 99%

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Machine Learning for Mineral Identification and Ore Estimation from Hyperspectral Imagery in Tin–Tungsten Deposits: Simulation under Indoor Conditions

Lobo

García²,

Barroso

et al. 2021

Remote Sensing

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This study aims to assess the feasibility of delineating and identifying mineral ores from hyperspectral images of tin–tungsten mine excavation faces using machine learning classification. We compiled a set of hand samples of minerals of interest from a tin–tungsten mine and analyzed two types of hyperspectral images: (1) images acquired with a laboratory set-up under close-to-optimal conditions, and (2) a scan of a simulated mine face using a field set-up, under conditions closer to those in the gallery. We have analyzed the following minerals: cassiterite (tin ore), wolframite (tungsten ore), chalcopyrite, malachite, muscovite, and quartz. Classification (Linear Discriminant Analysis, Singular Vector Machines and Random Forest) of laboratory spectra had a very high overall accuracy rate (98%), slightly lower if the 450–950 nm and 950–1650 nm ranges are considered independently, and much lower (74.5%) for simulated conventional RGB imagery. Classification accuracy for the simulation was lower than in the laboratory but still high (85%), likely a consequence of the lower spatial resolution. All three classification methods performed similarly in this case, with Random Forest producing results of slightly higher accuracy. The user’s accuracy for wolframite was 85%, but cassiterite was often confused with wolframite (user’s accuracy: 70%). A lumped ore category achieved 94.9% user’s accuracy. Our study confirms the suitability of hyperspectral imaging to record the spatial distribution of ore mineralization in progressing tungsten–tin mine faces.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Machine Learning for Mineral Identification and Ore Estimation from Hyperspectral Imagery in Tin–Tungsten Deposits: Simulation under Indoor Conditions

Lobo

García²,

Barroso

et al. 2021

Remote Sensing

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“…We generate initial mineral maps from the enhanced hyperspectral data using a wellestablished and robust minimum wavelength mapper. We use the version available in the Hylite toolbox for spectral analysis [27]. The minimum wavelength mapper combines the position and depth information of the deepest absorption feature to provide an overview of its presence and distribution [10,28].…”

Section: Mineral Mappingmentioning

confidence: 99%

Resolution Enhancement for Drill-Core Hyperspectral Mineral Mapping

2021

Self Cite

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Drill-core samples are a key component in mineral exploration campaigns, and their rapid and objective analysis is becoming increasingly important. Hyperspectral imaging of drill-cores is a non-destructive technique that allows for non-invasive and fast mapping of mineral phases and alteration patterns. The use of adapted machine learning techniques such as supervised learning algorithms allows for a robust and accurate analysis of drill-core hyperspectral data. One of the remaining challenge is the spatial sampling of hyperspectral sensors in operational conditions, which does not allow us to render the textural and mineral diversity that is required to map minerals with low abundances and fine structures such as veins and faults. In this work, we propose a methodology in which we implement a resolution enhancement technique, a coupled non-negative matrix factorization, using hyperspectral, RGB images and high-resolution mineralogical data to produce mineral maps at higher spatial resolutions and to improve the mapping of minerals. The results demonstrate that the enhanced maps not only provide better details in the alteration patterns such as veins but also allow for mapping minerals that were previously hidden in the hyperspectral data due to its low spatial sampling.

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“…This paper presents application of scripting cartographic methods for topographic data analysis and modelling by GDAL, Geospatial Data Abstraction Library (GDAL/OGR contributors, 2020) and open-source GRASS GIS (Neteler, 2001;Neteler and Mitasova, 2008). Automated data processing in geological mapping is presented in the existing literature (Gruber et al, 2017;Maggiori et al, 2017;Krčmar et al, 2020;Bongiovanni et al, 2021;Thiele et al, 2021;Arriagada et al, 2021;Balogun et al, 2021). Using scripts and console based methods in cartography presented a new step in further developing of cartographic methods and solutions for machine learning and automatization in GIS.…”

Section: Introductionmentioning

confidence: 99%

Okinawa Trough geophysical and topographic modeling by GDAL utilities and GRASS GIS

Lemenkova

2021

Podzemni radovi

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This paper presents using GDAL utilities and GRASS GIS for topographic analysis of the raster grids based on GEBCO DEM as NetCDF file at 15 arc-second intervals. The focus study area encompasses the area around Okinawa Trough, Ryukyu trench-arc system, southern Japan, East China Sea and the Philippine Sea, West Pacific Ocean. Several GDAL utilities were applied for data processing: gdaldem, gdalwarp, gdalinfo, gdal_translate. The data were imported to GRASS GIS via r.in.gdal. Data visualization highlighted high resolution and accuracy of GEBCO grid, enabling topographic modelling at the advanced level. The algorithm of DEM processing, implemented in GDAL utility gdaldem, was used for generating multi-purpose topographic models: aspect, hillshade, roughness and topographic indices, such as Topographic Position Index (TPI), Terrain Ruggedness Index (TRI). Thematic maps (topography, geoid, marine free-air gravity) were visualized using GRASS GIS modules for raster (d.rast, r.colors, r.contour) and vector (d.vect, v.in.region, d.legend) data processing. The results demonstrated smoother bathymetry in the East China Sea and rugged relief in the Philippine Sea which corresponds to their different geological and geophysical settings. The presented methodology of the topographic analysis based on DEM demonstrated technical aspects of GDAL and GRASS as scripting approach of advanced cartography.

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Multi-scale, multi-sensor data integration for automated 3-D geological mapping

Cited by 56 publications

References 43 publications

Machine Learning for Mineral Identification and Ore Estimation from Hyperspectral Imagery in Tin–Tungsten Deposits: Simulation under Indoor Conditions

Machine Learning for Mineral Identification and Ore Estimation from Hyperspectral Imagery in Tin–Tungsten Deposits: Simulation under Indoor Conditions

Resolution Enhancement for Drill-Core Hyperspectral Mineral Mapping

Okinawa Trough geophysical and topographic modeling by GDAL utilities and GRASS GIS

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