Appendices for Geothermal Exploration Artificial Intelligence Report
The Geothermal Exploration Artificial Intelligence looks to use machine learning to spot geothermal identifiers from land maps. This is done to remotely detect geothermal sites for the purpose of energy uses. Such uses include enhanced geothermal system (EGS) applications, especially regarding finding locations for viable EGS sites. This submission includes the appendices and reports formerly attached to the Geothermal Exploration Artificial Intelligence Quarterly and Final Reports.
The appendices below include methodologies, results, and some data regarding what was used to train the Geothermal Exploration AI. The methodology reports explain how specific anomaly detection modes were selected for use with the Geo Exploration AI. This also includes how the detection mode is useful for finding geothermal sites. Some methodology reports also include small amounts of code. Results from these reports explain the accuracy of methods used for the selected sites (Brady Desert Peak and Salton Sea). Data from these detection modes can be found in some of the reports, such as the Mineral Markers Maps, but most of the raw data is included the DOE Database which includes Brady, Desert Peak, and Salton Sea Geothermal Sites.
Citation Formats
TY - DATA
AB - The Geothermal Exploration Artificial Intelligence looks to use machine learning to spot geothermal identifiers from land maps. This is done to remotely detect geothermal sites for the purpose of energy uses. Such uses include enhanced geothermal system (EGS) applications, especially regarding finding locations for viable EGS sites. This submission includes the appendices and reports formerly attached to the Geothermal Exploration Artificial Intelligence Quarterly and Final Reports.
The appendices below include methodologies, results, and some data regarding what was used to train the Geothermal Exploration AI. The methodology reports explain how specific anomaly detection modes were selected for use with the Geo Exploration AI. This also includes how the detection mode is useful for finding geothermal sites. Some methodology reports also include small amounts of code. Results from these reports explain the accuracy of methods used for the selected sites (Brady Desert Peak and Salton Sea). Data from these detection modes can be found in some of the reports, such as the Mineral Markers Maps, but most of the raw data is included the DOE Database which includes Brady, Desert Peak, and Salton Sea Geothermal Sites.
AU - Duzgun, H. Sebnem
A2 - Soydan, Hilal
A3 - Cavur, Mahmut
A4 - Moraga, Jim
A5 - Jin, Ge
DB - Open Energy Data Initiative (OEDI)
DP - Open EI | National Renewable Energy Laboratory
DO - 10.15121/1797280
KW - geothermal
KW - energy
KW - artificial intelligence
KW - hydrothermally altered minerals
KW - mineral markers
KW - SVM
KW - geodatabase
KW - well
KW - fault
KW - seismic
KW - AI
KW - border
KW - Brady
KW - Desert Peak
KW - Salton Sea
KW - land surface temperature
KW - deformation
KW - geophysical
KW - geophysics
KW - support vector machine
KW - hyperspectral
KW - hyperspectral imaging
KW - California
KW - Nevada
KW - EGS
KW - blind
KW - blind system
KW - deep learning
KW - machine learning
KW - exploration
KW - geospatial data
KW - short wavelength infrared
KW - SWIR
KW - database
KW - anomaly detection
KW - site detection
KW - radar
KW - hydrothermal
KW - model
KW - conceptual model
KW - Zotero
KW - raw data
KW - preproccessed
KW - processed data
KW - enhanced geothermal system
KW - engineered geothermal system
KW - remote sensing
KW - ArcGis
KW - GIS
KW - InSAR
KW - Morphology
KW - Morphological
KW - morphological features
KW - TIR
KW - VNIR
KW - visible near infrared
KW - thermal infrared
KW - code
KW - Python
LA - English
DA - 2021/01/08
PY - 2021
PB - Colorado School of Mines
T1 - Appendices for Geothermal Exploration Artificial Intelligence Report
UR - https://doi.org/10.15121/1797280
ER -
Duzgun, H. Sebnem, et al. Appendices for Geothermal Exploration Artificial Intelligence Report. Colorado School of Mines, 8 January, 2021, GDR. https://doi.org/10.15121/1797280.
Duzgun, H., Soydan, H., Cavur, M., Moraga, J., & Jin, G. (2021). Appendices for Geothermal Exploration Artificial Intelligence Report. [Data set]. GDR. Colorado School of Mines. https://doi.org/10.15121/1797280
Duzgun, H. Sebnem, Hilal Soydan, Mahmut Cavur, Jim Moraga, and Ge Jin. Appendices for Geothermal Exploration Artificial Intelligence Report. Colorado School of Mines, January, 8, 2021. Distributed by GDR. https://doi.org/10.15121/1797280
@misc{OEDI_Dataset_7421,
title = {Appendices for Geothermal Exploration Artificial Intelligence Report},
author = {Duzgun, H. Sebnem and Soydan, Hilal and Cavur, Mahmut and Moraga, Jim and Jin, Ge},
abstractNote = {The Geothermal Exploration Artificial Intelligence looks to use machine learning to spot geothermal identifiers from land maps. This is done to remotely detect geothermal sites for the purpose of energy uses. Such uses include enhanced geothermal system (EGS) applications, especially regarding finding locations for viable EGS sites. This submission includes the appendices and reports formerly attached to the Geothermal Exploration Artificial Intelligence Quarterly and Final Reports.
The appendices below include methodologies, results, and some data regarding what was used to train the Geothermal Exploration AI. The methodology reports explain how specific anomaly detection modes were selected for use with the Geo Exploration AI. This also includes how the detection mode is useful for finding geothermal sites. Some methodology reports also include small amounts of code. Results from these reports explain the accuracy of methods used for the selected sites (Brady Desert Peak and Salton Sea). Data from these detection modes can be found in some of the reports, such as the Mineral Markers Maps, but most of the raw data is included the DOE Database which includes Brady, Desert Peak, and Salton Sea Geothermal Sites.},
url = {https://gdr.openei.org/submissions/1303},
year = {2021},
howpublished = {GDR, Colorado School of Mines, https://doi.org/10.15121/1797280},
note = {Accessed: 2025-05-29},
doi = {10.15121/1797280}
}
https://dx.doi.org/10.15121/1797280
Details
Data from Jan 8, 2021
Last updated Jan 13, 2022
Submitted Apr 25, 2021
Organization
Colorado School of Mines
Contact
Jim Moraga
303.273.3768
Authors
Original Source
https://gdr.openei.org/submissions/1303Research Areas
Keywords
geothermal, energy, artificial intelligence, hydrothermally altered minerals, mineral markers, SVM, geodatabase, well, fault, seismic, AI, border, Brady, Desert Peak, Salton Sea, land surface temperature, deformation, geophysical, geophysics, support vector machine, hyperspectral, hyperspectral imaging, California, Nevada, EGS, blind, blind system, deep learning, machine learning, exploration, geospatial data, short wavelength infrared, SWIR, database, anomaly detection, site detection, radar, hydrothermal, model, conceptual model, Zotero, raw data, preproccessed, processed data, enhanced geothermal system, engineered geothermal system, remote sensing, ArcGis, GIS, InSAR, Morphology, Morphological, morphological features, TIR, VNIR, visible near infrared, thermal infrared, code, PythonDOE Project Details
Project Name Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning
Project Lead Mike Weathers
Project Number EE0008760
