GIS Resource Compilation Map Package - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

Brown, Stephen; Fehler, Michael; Coolbaugh, Mark; Treitel, Sven; Faulds, James; Ayling, Bridget; Lindsey, Cary; Micander, Rachel; Mlawsky, Eli; Smith, Connor; Queen, John; Gu, Chen; Akerley, John; DeAngelo, Jacob; Glen, Jonathan; Siler, Drew; Burns, Erick; Warren, Ian

doi:10.15121/1897037

Title: GIS Resource Compilation Map Package - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

Dataset
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Abstract

This submission contains an ESRI map package (.mpk) with an embedded geodatabase for GIS resources used or derived in the Nevada Machine Learning project, meant to accompany the final report. The package includes layer descriptions, layer grouping, and symbology. Layer groups include: new/revised datasets (paleo-geothermal features, geochemistry, geophysics, heat flow, slip and dilation, potential structures, geothermal power plants, positive and negative test sites), machine learning model input grids, machine learning models (Artificial Neural Network (ANN), Extreme Learning Machine (ELM), Bayesian Neural Network (BNN), Principal Component Analysis (PCA/PCAk), Non-negative Matrix Factorization (NMF/NMFk) - supervised and unsupervised), original NV Play Fairway data and models, and NV cultural/reference data. See layer descriptions for additional metadata. Smaller GIS resource packages (by category) can be found in the related datasets section of this submission. A submission linking the full codebase for generating machine learning output models is available through the "Related Datasets" link on this page, and contains results beyond the top picks present in this compilation.

Authors:

Brown, Stephen; Fehler, Michael; Coolbaugh, Mark; Treitel, Sven;

;

; Lindsey, Cary; Micander, Rachel; Mlawsky, Eli; Smith, Connor; Queen, John; Gu, Chen; Akerley, John; DeAngelo, Jacob; Glen, Jonathan; Siler, Drew; Burns, Erick; Warren, Ian

Nevada Bureau of Mines and Geology

Publication Date:: Tue Jun 01 04:00:00 UTC 2021

Other Number(s):: 1350

DOE Contract Number:: EE0008762

Research Org.:: DOE Geothermal Data Repository; Nevada Bureau of Mines and Geology

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

Collaborations:: Nevada Bureau of Mines and Geology

Subject:: 15 GEOTHERMAL ENERGY; ANN; BNN; ELM; GIS; Machine Learning; Map Package; NMF; Nevada; PCA; PFA; Play Fairway; characterization; cultural; data; dilation; dlip; energy; exploration; geochemistry; geodatabase; geophysics; geothermal; great basin; heat flow; hydrothermal; models; paleo-geothermal features; processed data; slip and dilation; structure; supervised; test sittes; unsupervised

OSTI Identifier:: 1897037

DOI:: https://doi.org/10.15121/1897037

Citation Formats


                    Brown, Stephen, Fehler, Michael, Coolbaugh, Mark, Treitel, Sven, Faulds, James, Ayling, Bridget, Lindsey, Cary, Micander, Rachel, Mlawsky, Eli, Smith, Connor, Queen, John, Gu, Chen, Akerley, John, DeAngelo, Jacob, Glen, Jonathan, Siler, Drew, Burns, Erick, and Warren, Ian. GIS Resource Compilation Map Package - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada.  United States: N. p., 2021. 
        Web.  doi:10.15121/1897037.

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                    Brown, Stephen, Fehler, Michael, Coolbaugh, Mark, Treitel, Sven, Faulds, James, Ayling, Bridget, Lindsey, Cary, Micander, Rachel, Mlawsky, Eli, Smith, Connor, Queen, John, Gu, Chen, Akerley, John, DeAngelo, Jacob, Glen, Jonathan, Siler, Drew, Burns, Erick, & Warren, Ian. GIS Resource Compilation Map Package - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada.  United States.  doi:https://doi.org/10.15121/1897037

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                    Brown, Stephen, Fehler, Michael, Coolbaugh, Mark, Treitel, Sven, Faulds, James, Ayling, Bridget, Lindsey, Cary, Micander, Rachel, Mlawsky, Eli, Smith, Connor, Queen, John, Gu, Chen, Akerley, John, DeAngelo, Jacob, Glen, Jonathan, Siler, Drew, Burns, Erick, and Warren, Ian. 2021.  
"GIS Resource Compilation Map Package - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada".  United States.  doi:https://doi.org/10.15121/1897037.  https://www.osti.gov/servlets/purl/1897037. Pub date:Tue Jun 01 04:00:00 UTC 2021

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@article{osti_1897037,

  title        = {GIS Resource Compilation Map Package - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada},

  author       = {Brown, Stephen and Fehler, Michael and Coolbaugh, Mark and Treitel, Sven and Faulds, James and Ayling, Bridget and Lindsey, Cary and Micander, Rachel and Mlawsky, Eli and Smith, Connor and Queen, John and Gu, Chen and Akerley, John and DeAngelo, Jacob and Glen, Jonathan and Siler, Drew and Burns, Erick and Warren, Ian},

  abstractNote = {This submission contains an ESRI map package (.mpk) with an embedded geodatabase for GIS resources used or derived in the Nevada Machine Learning project, meant to accompany the final report. The package includes layer descriptions, layer grouping, and symbology. Layer groups include: new/revised datasets (paleo-geothermal features, geochemistry, geophysics, heat flow, slip and dilation, potential structures, geothermal power plants, positive and negative test sites), machine learning model input grids, machine learning models (Artificial Neural Network (ANN), Extreme Learning Machine (ELM), Bayesian Neural Network (BNN), Principal Component Analysis (PCA/PCAk), Non-negative Matrix Factorization (NMF/NMFk) - supervised and unsupervised), original NV Play Fairway data and models, and NV cultural/reference data. See layer descriptions for additional metadata. Smaller GIS resource packages (by category) can be found in the related datasets section of this submission. A submission linking the full codebase for generating machine learning output models is available through the "Related Datasets" link on this page, and contains results beyond the top picks present in this compilation.},

  doi          = {10.15121/1897037},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Tue Jun 01 04:00:00 UTC 2021},

  month        = {Tue Jun 01 04:00:00 UTC 2021}

}

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DOI: https://doi.org/10.15121/1897037

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