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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

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 -
Export Citation to RIS
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: 2025-05-03}, 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

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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