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Appendices for Geothermal Exploration Artificial Intelligence Report

Publicly accessible License 

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.

DOE Geodatabases.zip

Geodatabases for Brady, Desert Peak, and Salton Sea Geothermal Sites. Needs o be used with ArcGIS or other geodatabase compatible GIS software.
46

Deformation Analysis for Brady.docx

The Deformation Analysis Report outlines methods and technology used for identifying land deformations caused by geothermal activity. The report also includes descripti... more
196

Geodatabase Design.docx

Geodatabase Design outline for the Geothermal Exploration AI. Databases made using this design include Brady Hot Springs, Desert Peak and Salton Sea. The design outline... more
549

Geophysical Analysis Results.docx

Report on Geophysical analysis results for Brady Geothermal Field. Includes seismic mapping and fault line mapping.
68

Land Surface Temperature Report.docx

The Land Surface Temperature (LST) Report explains the reasoning behind using LST as an input to the Geothermal Exploration AI algorithm. Included in the document is da... more
210

Mineral Mapping Literature Report.docx

The Mineral Mapping Literature Report outlines the methods for remote sensing of geothermal sites and the application of these remote sensing methods. Methods include i... more
226

Mineral Marker Maps.docx

Maps displaying the results of the Mineral Markers analysis. Maps show anomalies product of hydrothermally altered minerals in the area of interest.
62

Mineral Markers Methodology.docx

Report with the Methodology used for using Mineral Markers layer in the Geothermal AI. Applies to Brady and Desert Peak Geothermal Areas. Includes information regarding... more
123

Mineral Markers References Zotero Format.zip

Mineral Markers literature references in the Zotero format. Within the zip there are Zotero cache files along with links to each literature reference.
25

Morphology Literature.docx

Morphology Literature Report for Brady Geothermal Field. This report includes information regarding the use of morphological features as geothermal site indicators for ... more
139

Support Vector Machine Methodology.docx

Support Vector Machine (SVM) applied to the geothermal exploration report. This report explains why SVM was used with the Geothermal Exploration Artificial Intelligenc... more
177

Well Fault and Seismic Borders Report.docx

Report about the borders for well, fault, and seismic data. Fault density data was used to define analysis boundaries. The report includes fault data for the three site... more
207

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: 2025-07-22}, 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

jmoraga@mines.edu

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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