Geochemistry and paleo-geothermal features - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

Faulds, James; Ayling, Bridget; DeAngelo, Jacob

doi:10.15121/1832126

Title: Geochemistry and paleo-geothermal features - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

Dataset
Other Related Research

Abstract

This submission contains the geochemistry dataset and paleo-geothermal features (sinter, travertine, tufa) (shapefiles and symbology) used in the Nevada Geothermal Machine Learning project. A submission linking the full GitHub repository for our machine learning Jupyter Notebooks will appear in the related datasets section of this page once available.

Authors:

;

; DeAngelo, Jacob

Nevada Bureau of Mines and Geology

Publication Date:: Thu Jan 01 04:00:00 UTC 1970

Other Number(s):: 1352

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; Machine Learning; Nevada; Play Fairway; code; energy; geochemistry; geophysics; geospatial; geospatial data; geothermal; geothermometry; paleo-geothermal; sinter; travertine; tufa

OSTI Identifier:: 1832126

DOI:: https://doi.org/10.15121/1832126

Citation Formats


                    Faulds, James, Ayling, Bridget, and DeAngelo, Jacob. Geochemistry and paleo-geothermal features - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada.  United States: N. p., 1970. 
        Web.  doi:10.15121/1832126.

Copy to clipboard


                    Faulds, James, Ayling, Bridget, & DeAngelo, Jacob. Geochemistry and paleo-geothermal features - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada.  United States.  doi:https://doi.org/10.15121/1832126

Copy to clipboard


                    Faulds, James, Ayling, Bridget, and DeAngelo, Jacob. 1970.  
"Geochemistry and paleo-geothermal features - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada".  United States.  doi:https://doi.org/10.15121/1832126.  https://www.osti.gov/servlets/purl/1832126. Pub date:Thu Jan 01 04:00:00 UTC 1970

Copy to clipboard


                    
@article{osti_1832126,

  title        = {Geochemistry and paleo-geothermal features - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada},

  author       = {Faulds, James and Ayling, Bridget and DeAngelo, Jacob},

  abstractNote = {This submission contains the geochemistry dataset and paleo-geothermal features (sinter, travertine, tufa) (shapefiles and symbology) used in the Nevada Geothermal Machine Learning project. A submission linking the full GitHub repository for our machine learning Jupyter Notebooks will appear in the related datasets section of this page once available.},

  doi          = {10.15121/1832126},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Thu Jan 01 04:00:00 UTC 1970},

  month        = {Thu Jan 01 04:00:00 UTC 1970}

}

Copy to clipboard

Dataset:

View Dataset

DOI: https://doi.org/10.15121/1832126

Save / Share:

Export Metadata

Save to My Library

Similar records in DOE Data Explorer and OSTI.GOV collections:

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

Dataset Faulds, James ; Coolbaugh, Mark

This submission contains shapefiles, geotiffs, and symbology for the revised-from-Play-Fairway potential structures/structural settings used in the Nevada Geothermal Machine Learning project. Layers include potential structural setting ellipses, centroids, and distance-to-centroid raster. A submission linking the full GitHub repository for our machine learning Jupyter Notebooks will appear in the related datasets section of this page once available.
Machine Learning Model Geotiffs - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

Dataset Faulds, James ; Brown, Stephen ; Smith, Connor ; ...

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.
Python Codebase and Jupyter Notebooks - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

Dataset Brown, Steve ; Smith, Connor

Git archive containing Python modules and resources used to generate machine-learning models used in the "Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada" project. This software is licensed as free to use, modify, and distribute with attribution. Full license details are included within the archive. See "documentation.zip" for setup instructions and file trees annotated with module descriptions.
GIS Resource Compilation Map Package - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

Dataset Brown, Stephen ; Fehler, Michael ; Coolbaugh, Mark ; ...

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 datamore »« less
Nevada Great Basin Play Fairway Analysis Regional Data

Dataset Faulds, James

This project focused on defining geothermal play fairways and development of a detailed geothermal potential map of a large transect across the Great Basin region (96,000 km2), with the primary objective of facilitating discovery of commercial-grade, blind geothermal fields (i.e. systems with no surface hot springs or fumaroles) and thereby accelerating geothermal development in this promising region. Data included in this submission consists of: structural settings (target areas, recency of faulting, slip and dilation potential, slip rates, quality), regional-scale strain rates, earthquake density and magnitude, gravity data, temperature at 3 km depth, permeability models, favorability models, degree of exploration andmore »« less

Similar Records