GIS Resource Compilation Map Package - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada
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 -
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
Original Source
https://gdr.openei.org/submissions/1350Research Areas
Keywords
geothermal, energy, Nevada, Machine Learning, Map Package, GIS, PCA, NMF, BNN, ANN, ELM, geochemistry, geophysics, heat flow, slip and dilation, structure, Play Fairway, PFA, exploration, characterization, great basin, dlip, dilation, geodatabase, hydrothermal, data, models, processed data, paleo-geothermal features, test sittes, supervised, unsupervised, culturalDOE 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