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
Colorado School of Mines. (2021). Appendices for Geothermal Exploration Artificial Intelligence Report [data set]. Retrieved from https://dx.doi.org/10.15121/1797280.
Duzgun, H. Sebnem, Soydan, Hilal, Cavur, Mahmut, Moraga, Jim, and Jin, Ge. Appendices for Geothermal Exploration Artificial Intelligence Report. United States: N.p., 08 Jan, 2021. Web. doi: 10.15121/1797280.
Duzgun, H. Sebnem, Soydan, Hilal, Cavur, Mahmut, Moraga, Jim, & Jin, Ge. Appendices for Geothermal Exploration Artificial Intelligence Report. United States. https://dx.doi.org/10.15121/1797280
Duzgun, H. Sebnem, Soydan, Hilal, Cavur, Mahmut, Moraga, Jim, and Jin, Ge. 2021. "Appendices for Geothermal Exploration Artificial Intelligence Report". United States. https://dx.doi.org/10.15121/1797280. https://gdr.openei.org/submissions/1303.
@div{oedi_1303, title = {Appendices for Geothermal Exploration Artificial Intelligence Report}, author = {Duzgun, H. Sebnem, Soydan, Hilal, Cavur, Mahmut, 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.}, doi = {10.15121/1797280}, url = {https://gdr.openei.org/submissions/1303}, journal = {}, number = , volume = , place = {United States}, year = {2021}, month = {01}}
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
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