Mapping hydrothermal and supergene alteration zones associated with carbonate-hosted Zn-Pb deposits by using PRISMA satellite imagery supported by field-based hyperspectral data, mineralogical and geochemical analysis

Chirico, Rita; Mondillo, Nicola; Laukamp, Carsten; Mormone, Angela; Martire, Diego Di; Novellino, Alessandro; Balassone, Giuseppina

doi:10.1016/j.oregeorev.2022.105244

Cited by 21 publications

(8 citation statements)

References 71 publications

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“…They integrated the results with geochemical, geophysical and geological evidence maps using the fuzzy logic to generate a drilling favourability map. The new generation of spaceborne hyperspectral imaging such as PRISMA, EnMAP, and GaoFen-5 (GF-5) are also predominantly used for mineral exploration and alteration/lithologic mapping purposes [98,[104][105][106][107]. Yet, the high spectral resolution (<10nm) and high SNR (>400:1) of these imaging systems can find numerous applications within the mining sector, as outlined in this paper.…”

Section: Exploration Sectormentioning

confidence: 99%

Imaging Spectroscopy for the Mining Life Cycle: A Guide for Drone Applications

Koerting,

Asadzadeh,

Hildebrand

et al. 2024

Preprint

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Hyperspectral imaging data holds great potential for the different stages of the mining life cycle in active and post-mining environments. However, the technology has yet to reach the stage of large-scale industrial implementation and acceptance. While hyperspectral satellite imagery can achieve high spectral resolution, signal-to-noise ratio (SNR) and global availability with break-through satellite systems like EnMAP, EMIT and PRISMA, limited spatial resolution poses chal-lenges for sectors like mining, which require decimetre to centimetre scale resolution for applica-tions such as reconciliation, ore/waste estimates, geotechnical assessments and environmental monitoring. Hyperspectral imaging from drones (referred to herein as Uncrewed Aerial Systems; UASs) offers high spatial resolution data relevant to the camp/ mine scale, with the capability for frequent, user-defined re-visit times. This has been made possible by the miniaturization of hy-perspectral imaging systems. Collection of data in the visible to near and shortwave infrared (VNIR-SWIR) wavelength regions enables the detection of different minerals and surface altera-tion patterns potentially revealing crucial information for exploration, extraction, re-mining, waste remediation, and rehabilitation. In this paper, we provide a review of relevant studies de-ploying hyperspectral imaging in or applicable to the mining sector, especially for the use of hy-perspectral VNIR-SWIR Uncrewed Aerial Systems. Where required, we draw on previous in-sights derived from satellite or ground-based systems. We also discuss UAS survey planning, and sampling considerations for validation and interpretation.

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Section: Exploration Sectormentioning

confidence: 99%

Imaging Spectroscopy for the Mining Life Cycle: A Guide for Drone Applications

Koerting,

Asadzadeh,

Hildebrand

et al. 2024

Preprint

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“…It enables the capture of images with a superior signal-to-noise ratio (SNR) compared to the Hyperion sensor. Essentially, it serves as a satellite-based earth observation mission designed to provide spectroscopic imagery, fostering the development of innovative methods and applications for the management and analysis of ore mineral resources [2,[7][8][9][10]. The coexistence of minerals with similar spectral properties often leads to spectral mixing, resulting in overlapping absorption regions and highly correlated spectra [11,12].…”

Section: Introductionmentioning

confidence: 99%

“…These challenges, including diverse spectral features and limited ground samples, hinder the function of image processing algorithms and remote sensing sensors. Addressing these issues, the use of PRISMA hyperspectral imagery in mineral exploration across various geographic and geological settings highlights the adaptability and effectiveness of PRISMA imagery in overcoming challenges posed by spectral mixing, light scattering effects, and intricate spectral patterns [2,[7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…The Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), Independent Component Analysis (ICA), Adaptive Coherence Estimator (ACE), Random Forest (RF), XGboost (XGB), Support Vector Machine (SVM) and many other machine-learningbased classification algorithms were used for processing PRISMA data in mapping alteration minerals, identifying economic mineralization prospects, delineating dolomitization, obtaining high-quality reflectance estimations and achieving high-accuracy lithological mapping [2,[7][8][9][10][14][15][16][17][18][19]. Additionally, novel approaches and algorithms such as the spectral hourglass, iterative informed spectral unmixing technique, fuzzy logic approach, GIS-based algorithm, and informed linear mixing model were implemented to PRISMA data to determine the potential of the dataset for automated alteration mineral identification in diverse environments [20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

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PRISMA Hyperspectral Remote Sensing Data for Mapping Alteration Minerals in Sar-e-Châh-e-Shur Region, Birjand, Iran

Habashi,

Jamshid Moghadam,

Mohammady Oskouei

et al. 2024

Remote Sensing

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Remote sensing satellite imagery consistently provides valuable and frequent information, enabling the exploration of mineral resources across immense, remote and harsh domains. Recent developments in spaceborne hyperspectral remote sensing have opened avenues to support diverse remote sensing applications, particularly in the realm of mineral exploration. This study evaluates the capabilities of the PRecursore IperSpettrale della Missione Applicativa (PRISMA) hyperspectral satellite data for mapping alteration minerals using the Matched Filtering Unmixing (MFU) approach in the Sar-e-châh-e-shur, Birjand, Iran. Minerals such as richterite, augite, psilomelane, ilmenite, kaolinite, smectite, mirabilite, muscovite, and chlorite were identified using the vertex component analysis (VCA) technique. Subsequently, alteration mineral maps of the study area were generated using a matched filtering technique. Additionally, through the integration of X-ray diffraction (XRD) analysis, thin section examination, geochemical study of stream sediments, and interpretation of geological maps, potential alteration mineralization zones were delineated in the study area. Ultimately, the validation process, which included comparing the maps with the findings derived from the PRISMA remote sensing study, was conducted using the normal score equation. Thus, our results yielded a normalized score of 3.42 out of 4, signifying an 85.71% agreement with the regional geological characteristics of the study area. The results of this investigation highlight the substantial potential of the PRISMA dataset for systematic alteration mineral mapping and consequent exploration of ore minerals, specifically in challenging and inaccessible terrains.

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“…Spectral indices, such as the Normalized Difference Vegetation Index (NDVI) and the Soil Adjusted Vegetation Index (SAVI) [12], utilize specific band ratios in multispectral data to highlight certain features, such as vegetation or water bodies. Band ratios can also be used to detect geological features of interest for mining exploration or open-pit surveys [13][14][15][16]. Unsupervised classification methods like K-means or ISODATA (Iterative Self-Organizing Data Analysis Technique) apply clustering algorithms to group pixels with similar spectral properties, requiring minimal human intervention [17,18].…”

Section: Introductionmentioning

confidence: 99%

Synthetic Data for Sentinel-2 Semantic Segmentation

Clabaut,

Foucher,

Bouroubi

et al. 2024

Remote Sensing

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Satellite observations provide critical data for a myriad of applications, but automated information extraction from such vast datasets remains challenging. While artificial intelligence (AI), particularly deep learning methods, offers promising solutions for land cover classification, it often requires massive amounts of accurate, error-free annotations. This paper introduces a novel approach to generate a segmentation task dataset with minimal human intervention, thus significantly reducing annotation time and potential human errors. ‘Samples’ extracted from actual imagery were utilized to construct synthetic composite images, representing 10 segmentation classes. A DeepResUNet was solely trained on this synthesized dataset, eliminating the need for further fine-tuning. Preliminary findings demonstrate impressive generalization abilities on real data across various regions of Quebec. We endeavored to conduct a quantitative assessment without reliance on manually annotated data, and the results appear to be comparable, if not superior, to models trained on genuine datasets.

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Mapping hydrothermal and supergene alteration zones associated with carbonate-hosted Zn-Pb deposits by using PRISMA satellite imagery supported by field-based hyperspectral data, mineralogical and geochemical analysis

Cited by 21 publications

References 71 publications

Imaging Spectroscopy for the Mining Life Cycle: A Guide for Drone Applications

Imaging Spectroscopy for the Mining Life Cycle: A Guide for Drone Applications

PRISMA Hyperspectral Remote Sensing Data for Mapping Alteration Minerals in Sar-e-Châh-e-Shur Region, Birjand, Iran

Synthetic Data for Sentinel-2 Semantic Segmentation

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