Appendices for Geothermal Exploration Artificial Intelligence Report

Abstract

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
Jan
2021
Data from January, 2021
Submitted Apr 25, 2021

Contact

Colorado School of Mines


303.273.3768

Status

Publicly accessible License 

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

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, Python

Share

Export Citation to RIS
Submission Downloads