Articles | Volume 14, issue 4
https://doi.org/10.5194/se-14-463-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/se-14-463-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Apr 2023
Research article |
| 27 Apr 2023
| 27 Apr 2023
Spectral characterisation of hydrothermal alteration associated with sediment-hosted Cu–Ag mineralisation in the central European Kupferschiefer
Léa Géring
CORRESPONDING AUTHOR
Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for
Resource Technology, 09599 Freiberg, Germany
Moritz Kirsch
Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for
Resource Technology, 09599 Freiberg, Germany
Samuel Thiele
Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for
Resource Technology, 09599 Freiberg, Germany
Andréa De Lima Ribeiro
Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for
Resource Technology, 09599 Freiberg, Germany
Richard Gloaguen
Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for
Resource Technology, 09599 Freiberg, Germany
Jens Gutzmer
Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for
Resource Technology, 09599 Freiberg, Germany
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Cited
15 citations as recorded by crossref.
- Maximising the value of hyperspectral drill core scanning through real-time processing and analysis S. Thiele et al. https://doi.org/10.3389/feart.2024.1433662
- MWIR hyperspectral-based mineral abundance mapping and profiling in sedimentary phosphate drill cores: Implications for phosphate geology and mining H. Lkhaoua et al. https://doi.org/10.1016/j.rineng.2026.113525
- Evaluation of the DROACOR Model for Atmospheric Correction of Drone Hyperspectral Data Y. Son https://doi.org/10.7780/kjrs.2024.40.6.1.31
- Predicting Mineralogy with Hyperspectral Data: A Benchmark Dataset and Machine Learning Framework to Enable Hyperspectral Geometallurgy S. Thiele et al. https://doi.org/10.3390/min16070674
- Near surface generation, burial recrystallization, and structural overprinting of carbonate platform dolomites G. Gairola et al. https://doi.org/10.1038/s41598-026-35353-4
- Emerging downhole sensing technologies for mineral exploration L. Senjoba et al. https://doi.org/10.1007/s44346-026-00023-w
- Hyperspectral Drill Core Spectroscopy for Alteration Zoning in Orogenic Gold Deposits: Insights from the Liba Deposit, West Qinling, China Z. Li et al. https://doi.org/10.3390/min16050526
- A data-driven hyperspectral method for sampling of diagenetic carbonate fabrics – A case study using an outcrop analogue of Jurassic Arab-D reservoirs, Saudi Arabia G. Gairola et al. https://doi.org/10.1016/j.marpetgeo.2024.106691
- Digital transformation in geotechnical site characterization: trends, methods, and future roadmaps Y. Hu et al. https://doi.org/10.1080/23311916.2026.2677996
- Multi source Earth observation and machine learning framework for mapping land degradation vulnerability in salinity- and hydrothermal-stressed regions of the southern main Ethiopian Rift T. Moged Reda et al. https://doi.org/10.1371/journal.pone.0359349
- Multiphysics property prediction from hyperspectral drill core data A. Kamath et al. https://doi.org/10.5194/se-16-351-2025
- The deportment of silver in Kupferschiefer ores at the Spremberg-Graustein-Schleife deposit, Germany Z. Nourizenouz et al. https://doi.org/10.1007/s00126-025-01377-5
- Integrated VNIR–SWIR Spectral, Mineralogical, and Geochemical Classification of Hydrothermal Alteration Zones in the Shadan Au–Cu System, Eastern Iran N. Nayebi et al. https://doi.org/10.3390/min16040379
- Detection limits of kaolinites and some common minerals in binary mixtures by short-wave infrared spectroscopy Á. Santamaría-López et al. https://doi.org/10.1016/j.clay.2024.107269
- Modern scanning- and micro-analytical spectroscopy – technologies and workflow to characterise critical raw materials: A case study of the Sokli Phosphate and Rare Earth Elements deposit, Finland E. Jolis et al. https://doi.org/10.1016/j.oregeorev.2025.106683
15 citations as recorded by crossref.
- Maximising the value of hyperspectral drill core scanning through real-time processing and analysis S. Thiele et al. https://doi.org/10.3389/feart.2024.1433662
- MWIR hyperspectral-based mineral abundance mapping and profiling in sedimentary phosphate drill cores: Implications for phosphate geology and mining H. Lkhaoua et al. https://doi.org/10.1016/j.rineng.2026.113525
- Evaluation of the DROACOR Model for Atmospheric Correction of Drone Hyperspectral Data Y. Son https://doi.org/10.7780/kjrs.2024.40.6.1.31
- Predicting Mineralogy with Hyperspectral Data: A Benchmark Dataset and Machine Learning Framework to Enable Hyperspectral Geometallurgy S. Thiele et al. https://doi.org/10.3390/min16070674
- Near surface generation, burial recrystallization, and structural overprinting of carbonate platform dolomites G. Gairola et al. https://doi.org/10.1038/s41598-026-35353-4
- Emerging downhole sensing technologies for mineral exploration L. Senjoba et al. https://doi.org/10.1007/s44346-026-00023-w
- Hyperspectral Drill Core Spectroscopy for Alteration Zoning in Orogenic Gold Deposits: Insights from the Liba Deposit, West Qinling, China Z. Li et al. https://doi.org/10.3390/min16050526
- A data-driven hyperspectral method for sampling of diagenetic carbonate fabrics – A case study using an outcrop analogue of Jurassic Arab-D reservoirs, Saudi Arabia G. Gairola et al. https://doi.org/10.1016/j.marpetgeo.2024.106691
- Digital transformation in geotechnical site characterization: trends, methods, and future roadmaps Y. Hu et al. https://doi.org/10.1080/23311916.2026.2677996
- Multi source Earth observation and machine learning framework for mapping land degradation vulnerability in salinity- and hydrothermal-stressed regions of the southern main Ethiopian Rift T. Moged Reda et al. https://doi.org/10.1371/journal.pone.0359349
- Multiphysics property prediction from hyperspectral drill core data A. Kamath et al. https://doi.org/10.5194/se-16-351-2025
- The deportment of silver in Kupferschiefer ores at the Spremberg-Graustein-Schleife deposit, Germany Z. Nourizenouz et al. https://doi.org/10.1007/s00126-025-01377-5
- Integrated VNIR–SWIR Spectral, Mineralogical, and Geochemical Classification of Hydrothermal Alteration Zones in the Shadan Au–Cu System, Eastern Iran N. Nayebi et al. https://doi.org/10.3390/min16040379
- Detection limits of kaolinites and some common minerals in binary mixtures by short-wave infrared spectroscopy Á. Santamaría-López et al. https://doi.org/10.1016/j.clay.2024.107269
- Modern scanning- and micro-analytical spectroscopy – technologies and workflow to characterise critical raw materials: A case study of the Sokli Phosphate and Rare Earth Elements deposit, Finland E. Jolis et al. https://doi.org/10.1016/j.oregeorev.2025.106683
Saved (final revised paper)
Latest update: 11 Nov 2026
Short summary
We apply multi-range hyperspectral imaging on drill core material from a Kupferschiefer-type Cu–Ag deposit in Germany, mapping minerals such as iron oxides, kaolinite, sulfate, and carbonates at millimetre resolution and in a rapid, cost-efficient, and continuous manner to track hydrothermal fluid flow paths and vectors towards base metal deposits in sedimentary basins.
We apply multi-range hyperspectral imaging on drill core material from a Kupferschiefer-type...
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