Appendices for Geothermal Exploration Artificial Intelligence Report

Moraga, Jim

doi:10.15121/1797280

Title: Appendices for Geothermal Exploration Artificial Intelligence Report

Dataset
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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.

Authors:

Moraga, Jim

Colorado School of Mines

Publication Date:: Fri Jan 08 04:00:00 UTC 2021

Other Number(s):: 1303

DOE Contract Number:: EE0008760

Research Org.:: DOE Geothermal Data Repository; Colorado School of Mines

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Program (EE-4G)

Collaborations:: Colorado School of Mines

Subject:: 15 GEOTHERMAL ENERGY; AI; ArcGis; Brady; California; Desert Peak; EGS; GIS; InSAR; Morphological; Morphology; Nevada; Python; SVM; SWIR; Salton Sea; TIR; VNIR; Zotero; anomaly detection; artificial intelligence; blind; blind system; border; code; conceptual model; database; deep learning; deformation; energy; engineered geothermal system; enhanced geothermal system; exploration; fault; geodatabase; geophysical; geophysics; geospatial data; geothermal; hydrothermal; hydrothermally altered minerals; hyperspectral; hyperspectral imaging; land surface temperature; machine learning; mineral markers; model; morphological features; preproccessed; processed data; radar; raw data; remote sensing; seismic; short wavelength infrared; site detection; support vector machine; thermal infrared; visible near infrared; well

OSTI Identifier:: 1797280

DOI:: https://doi.org/10.15121/1797280

Citation Formats


                    Moraga, Jim. Appendices for Geothermal Exploration Artificial Intelligence Report.  United States: N. p., 2021. 
        Web.  doi:10.15121/1797280.

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                    Moraga, Jim. Appendices for Geothermal Exploration Artificial Intelligence Report.  United States.  doi:https://doi.org/10.15121/1797280

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                    Moraga, Jim. 2021.  
"Appendices for Geothermal Exploration Artificial Intelligence Report".  United States.  doi:https://doi.org/10.15121/1797280.  https://www.osti.gov/servlets/purl/1797280. Pub date:Fri Jan 08 04:00:00 UTC 2021

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@article{osti_1797280,

  title        = {Appendices for Geothermal Exploration Artificial Intelligence Report},

  author       = {Moraga, Jim},

  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},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Fri Jan 08 04:00:00 UTC 2021},

  month        = {Fri Jan 08 04:00:00 UTC 2021}

}

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DOI: https://doi.org/10.15121/1797280

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