Coso Geothermal Spectral Library for Rocks and Minerals
An integrated open mineral spectral library designed to enhance the utility and precision of mineral spectral data for geothermal exploration, developed from a reliable and comprehensive digital dataset for seamless sharing by integrating field data, the USGS spectral library, and pertinent information obtained from Coso geothermal field (Coso) in California. The ASD FieldSpec portable spectrometer was utilized for collecting spectral data, which was subsequently analyzed using the THOR Material Identification tool in ENVI, The Spectral Geologist (TSG) software by CSIRO, and the Fully Constrained Linear Spectral Unmixing algorithm (FCLSU) in MATLAB. Scanning Electron Microscopy (SEM) with a mineralogy-analyzing function was employed to assess the mineral composition of samples, ensuring precise mineralogical analysis. A portable X-ray fluorescence (pXRF) spectrometer was also utilized to gather information on elemental enrichment. A framework for developing spectra data and establishing spectral libraries for various geological cases was proposed within this study. The characteristic spectra of six alteration minerals - alunite, chalcedony, epidote, hematite, kaolinite, and opal - were acquired from Coso samples. The spectral library for the Coso alteration minerals was introduced for further application in academic study or industrial exploration.
To browse the Coso Geothermal Spectral data and related figures from spreadsheets:
#1 Unzip and store the following items in the same folder.
'Contact Probe Data.zip', 'Sample Photos.zip', and 'Coso spectra of higher-certainity minerals.xlsx'.
#2 Open 'Coso spectra of higher-certainity minerals.xlsx'. The hyperlinks in the spreadsheet lead to the folders or figures of:
spectra .asd file, spectra ASC II file, spectra plots, and sample photos.
The spectra data is raw data without splice correction.
Spectra .asd files require particular software to open. (These cannot be opened in GIS software such as ArcGIS.)
Spectra ASC II files can be opened in a text editor or spread sheet program.
Citation Formats
Mining Engineering Department of Colorado School of Mines. (2023). Coso Geothermal Spectral Library for Rocks and Minerals [data set]. Retrieved from https://dx.doi.org/10.15121/1999403.
Cavur, Mahmut, Yu, Yu-Ting, Demir, Ebubekir, Duzgun, H. Sebnem, Pfaff, Katharina, Sabin, Andrew E., and Buck, Cliff. Coso Geothermal Spectral Library for Rocks and Minerals. United States: N.p., 23 Aug, 2023. Web. doi: 10.15121/1999403.
Cavur, Mahmut, Yu, Yu-Ting, Demir, Ebubekir, Duzgun, H. Sebnem, Pfaff, Katharina, Sabin, Andrew E., & Buck, Cliff. Coso Geothermal Spectral Library for Rocks and Minerals. United States. https://dx.doi.org/10.15121/1999403
Cavur, Mahmut, Yu, Yu-Ting, Demir, Ebubekir, Duzgun, H. Sebnem, Pfaff, Katharina, Sabin, Andrew E., and Buck, Cliff. 2023. "Coso Geothermal Spectral Library for Rocks and Minerals". United States. https://dx.doi.org/10.15121/1999403. https://gdr.openei.org/submissions/1528.
@div{oedi_1528, title = {Coso Geothermal Spectral Library for Rocks and Minerals}, author = {Cavur, Mahmut, Yu, Yu-Ting, Demir, Ebubekir, Duzgun, H. Sebnem, Pfaff, Katharina, Sabin, Andrew E., and Buck, Cliff.}, abstractNote = {An integrated open mineral spectral library designed to enhance the utility and precision of mineral spectral data for geothermal exploration, developed from a reliable and comprehensive digital dataset for seamless sharing by integrating field data, the USGS spectral library, and pertinent information obtained from Coso geothermal field (Coso) in California. The ASD FieldSpec portable spectrometer was utilized for collecting spectral data, which was subsequently analyzed using the THOR Material Identification tool in ENVI, The Spectral Geologist (TSG) software by CSIRO, and the Fully Constrained Linear Spectral Unmixing algorithm (FCLSU) in MATLAB. Scanning Electron Microscopy (SEM) with a mineralogy-analyzing function was employed to assess the mineral composition of samples, ensuring precise mineralogical analysis. A portable X-ray fluorescence (pXRF) spectrometer was also utilized to gather information on elemental enrichment. A framework for developing spectra data and establishing spectral libraries for various geological cases was proposed within this study. The characteristic spectra of six alteration minerals - alunite, chalcedony, epidote, hematite, kaolinite, and opal - were acquired from Coso samples. The spectral library for the Coso alteration minerals was introduced for further application in academic study or industrial exploration.
To browse the Coso Geothermal Spectral data and related figures from spreadsheets:
#1 Unzip and store the following items in the same folder.
'Contact Probe Data.zip', 'Sample Photos.zip', and 'Coso spectra of higher-certainity minerals.xlsx'.
#2 Open 'Coso spectra of higher-certainity minerals.xlsx'. The hyperlinks in the spreadsheet lead to the folders or figures of:
spectra .asd file, spectra ASC II file, spectra plots, and sample photos.
The spectra data is raw data without splice correction.
Spectra .asd files require particular software to open. (These cannot be opened in GIS software such as ArcGIS.)
Spectra ASC II files can be opened in a text editor or spread sheet program.}, doi = {10.15121/1999403}, url = {https://gdr.openei.org/submissions/1528}, journal = {}, number = , volume = , place = {United States}, year = {2023}, month = {08}}
https://dx.doi.org/10.15121/1999403
Details
Data from Aug 23, 2023
Last updated Jan 18, 2024
Submitted Aug 23, 2023
Organization
Mining Engineering Department of Colorado School of Mines
Contact
Mahmut Cavur
720.825.4069
Authors
Keywords
geothermal, energy, hydrothermal, spectroscopy, spectral database, Coso, geology, Coso geothermal field, GCF, California, spectral library, hydrothermal alteration, alteration minerals, spectral data, elemental enrichment, scanning electron microscopy, X-ray fluorescence, spectrometryDOE Project Details
Project Name Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning
Project Lead Mike Weathers
Project Number EE0008760