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
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.
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
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