Machine Learning Model Geotiffs - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

Faulds, James; Brown, Stephen; Smith, Connor; Queen, John; Treitel, Sven

doi:10.15121/1897036

Title: Machine Learning Model Geotiffs - 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 geotiffs, supporting shapefiles and readmes for the inputs and output models of algorithms explored in the Nevada Geothermal Machine Learning project, meant to accompany the final report. Layers include: Artificial Neural Network (ANN), Extreme Learning Machine (ELM), Bayesian Neural Network (BNN), Principal Component Analysis (PCA/PCAk), Non-negative Matrix Factorization (NMF/NMFk), input rasters of feature sets, and positive/negative training sites. See readme .txt files and final report for additional metadata. A submission linking the full codebase for generating machine learning output models is available under "related resources" on this page.

Authors:

; Brown, Stephen; Smith, Connor; Queen, John; Treitel, Sven

Nevada Bureau of Mines and Geology

Publication Date:: Tue Jun 01 00:00:00 EDT 2021

Other Number(s):: 1351

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; Algorithm; BNN; Bayesian; ELM; Great Basin; Machine Learning; NMF; Neural Network; Nevada; PCA; PFA; Play Fairway; Principal Component; characterization; energy; exploration; feature set; geothermal; geotiff; inputs; outputs; raster; training sites

OSTI Identifier:: 1897036

DOI:: https://doi.org/10.15121/1897036

Citation Formats


                    Faulds, James, Brown, Stephen, Smith, Connor, Queen, John, and Treitel, Sven. Machine Learning Model Geotiffs - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada.  United States: N. p., 2021. 
        Web.  doi:10.15121/1897036.

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                    Faulds, James, Brown, Stephen, Smith, Connor, Queen, John, & Treitel, Sven. Machine Learning Model Geotiffs - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada.  United States.  doi:https://doi.org/10.15121/1897036

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                    Faulds, James, Brown, Stephen, Smith, Connor, Queen, John, and Treitel, Sven. 2021.  
"Machine Learning Model Geotiffs - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada".  United States.  doi:https://doi.org/10.15121/1897036.  https://www.osti.gov/servlets/purl/1897036. Pub date:Tue Jun 01 00:00:00 EDT 2021

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@article{osti_1897036,

  title        = {Machine Learning Model Geotiffs - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada},

  author       = {Faulds, James and Brown, Stephen and Smith, Connor and Queen, John and Treitel, Sven},

  abstractNote = {This submission contains geotiffs, supporting shapefiles and readmes for the inputs and output models of algorithms explored in the Nevada Geothermal Machine Learning project, meant to accompany the final report. Layers include: Artificial Neural Network (ANN), Extreme Learning Machine (ELM), Bayesian Neural Network (BNN), Principal Component Analysis (PCA/PCAk), Non-negative Matrix Factorization (NMF/NMFk), input rasters of feature sets, and positive/negative training sites. See readme .txt files and final report for additional metadata. A submission linking the full codebase for generating machine learning output models is available under "related resources" on this page.},

  doi          = {10.15121/1897036},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Tue Jun 01 00:00:00 EDT 2021},

  month        = {Tue Jun 01 00:00:00 EDT 2021}

}

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DOI: https://doi.org/10.15121/1897036

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