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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

TY - DATA AB - 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. AU - Brown, Stephen A2 - Fehler, Michael A3 - Coolbaugh, Mark A4 - Treitel, Sven A5 - Faulds, James A6 - Ayling, Bridget A7 - Lindsey, Cary A8 - Micander, Rachel A9 - Mlawsky, Eli A10 - Smith, Connor A11 - Queen, John A12 - Gu, Chen A13 - Akerley, John A14 - DeAngelo, Jacob A15 - Glen, Jonathan A16 - Siler, Drew A17 - Burns, Erick A18 - Warren, Ian DB - Open Energy Data Initiative (OEDI) DP - Open EI | National Renewable Energy Laboratory DO - 10.15121/1897037 KW - geothermal KW - energy KW - Nevada KW - Machine Learning KW - Map Package KW - GIS KW - PCA KW - NMF KW - BNN KW - ANN KW - ELM KW - geochemistry KW - geophysics KW - heat flow KW - slip and dilation KW - structure KW - Play Fairway KW - PFA KW - exploration KW - characterization KW - great basin KW - dlip KW - dilation KW - geodatabase KW - hydrothermal KW - data KW - models KW - processed data KW - paleo-geothermal features KW - test sittes KW - supervised KW - unsupervised KW - cultural LA - English DA - 2021/06/01 PY - 2021 PB - Nevada Bureau of Mines and Geology T1 - GIS Resource Compilation Map Package - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada UR - https://doi.org/10.15121/1897037 ER -
Export Citation to RIS
Brown, Stephen, et al. GIS Resource Compilation Map Package - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada . Nevada Bureau of Mines and Geology, 1 June, 2021, GDR. https://doi.org/10.15121/1897037.
Brown, S., Fehler, M., Coolbaugh, M., Treitel, S., Faulds, J., Ayling, B., Lindsey, C., Micander, R., Mlawsky, E., Smith, C., Queen, J., Gu, C., Akerley, J., DeAngelo, J., Glen, J., Siler, D., Burns, E., & Warren, I. (2021). GIS Resource Compilation Map Package - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada . [Data set]. GDR. Nevada Bureau of Mines and Geology. https://doi.org/10.15121/1897037
Brown, Stephen, Michael Fehler, Mark Coolbaugh, Sven Treitel, James Faulds, Bridget Ayling, Cary Lindsey, Rachel Micander, Eli Mlawsky, Connor Smith, John Queen, Chen Gu, John Akerley, Jacob DeAngelo, Jonathan Glen, Drew Siler, Erick Burns, and Ian Warren. GIS Resource Compilation Map Package - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada . Nevada Bureau of Mines and Geology, June, 1, 2021. Distributed by GDR. https://doi.org/10.15121/1897037
@misc{OEDI_Dataset_7464, 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.}, url = {https://gdr.openei.org/submissions/1350}, year = {2021}, howpublished = {GDR, Nevada Bureau of Mines and Geology, https://doi.org/10.15121/1897037}, note = {Accessed: 2025-05-05}, doi = {10.15121/1897037} }
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

Research Areas

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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