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.

12 Resources

*downloads since 2019

Related Datasets

Datasets associated with the same DOE project
  Submission Name Resources Submitted Status

Additional Info

DOE Project Name: Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning
DOE Project Number: EE0008760
DOE Project Lead: Mike Weathers
DOI: 10.15121/1797280
Last Updated: 5 months ago
Data from January, 2021
Submitted Apr 25, 2021


Colorado School of Mines



Publicly accessible License 


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


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