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Appendices for Geothermal Exploration Artificial Intelligence Report

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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.
Export Citation to RIS
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_4088, 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

H. Sebnem Duzgun

Colorado School of Mines

Hilal Soydan

Colorado School of Mines

Mahmut Cavur

Kadir Has Universitesi

Jim Moraga

Colorado School of Mines

Ge Jin

Colorado School of Mines

Research Areas

DOE Project Details

Project Name Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning

Project Lead Mike Weathers

Project Number EE0008760

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