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

Publicly accessible License 

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.

Citation Formats

Nevada Bureau of Mines and Geology. (2021). GIS Resource Compilation Map Package - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada [data set]. Retrieved from https://dx.doi.org/10.15121/1897037.
Export Citation to RIS
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., 01 Jun, 2021. Web. doi: 10.15121/1897037.
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. https://dx.doi.org/10.15121/1897037
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. https://dx.doi.org/10.15121/1897037. https://gdr.openei.org/submissions/1350.
@div{oedi_1350, 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, 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.}, 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}, url = {https://gdr.openei.org/submissions/1350}, journal = {}, number = , volume = , place = {United States}, year = {2021}, month = {06}}
https://dx.doi.org/10.15121/1897037

Details

Data from Jun 1, 2021

Last updated Nov 7, 2022

Submitted Aug 25, 2022

Organization

Nevada Bureau of Mines and Geology

Contact

Elijah Mlawsky

775.682.9010

Authors

Stephen Brown

Massachusetts Institute of Technology

Michael Fehler

Massachusetts Institute of Technology

Mark Coolbaugh

Nevada Bureau of Mines and Geology

Sven Treitel

Hi-Q Geophysical Inc.

James Faulds

Nevada Bureau of Mines and Geology

Bridget Ayling

Nevada Bureau of Mines and Geology

Cary Lindsey

Nevada Bureau of Mines and Geology

Rachel Micander

Nevada Bureau of Mines and Geology

Eli Mlawsky

Nevada Bureau of Mines and Geology

Connor Smith

Nevada Bureau of Mines and Geology

John Queen

Hi-Q Geophysical

Chen Gu

Massachusetts Institute of Technology

John Akerley

Ormat

Jacob DeAngelo

USGS

Jonathan Glen

USGS

Drew Siler

USGS

Erick Burns

USGS

Ian Warren

Ormat

DOE Project Details

Project Name Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

Project Lead Mike Weathers

Project Number EE0008762

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