Machine Learning Model Geotiffs - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada
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.
Citation Formats
Nevada Bureau of Mines and Geology. (2021). Machine Learning Model Geotiffs - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada [data set]. Retrieved from https://dx.doi.org/10.15121/1897036.
Faulds, James, Brown, Stephen, Smith, Connor, Queen, John, and Treitel, Sven. Machine Learning Model Geotiffs - 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: 10.15121/1897036.
Faulds, James, Brown, Stephen, Smith, Connor, Queen, John, & Treitel, Sven. Machine Learning Model Geotiffs - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada. United States. https://dx.doi.org/10.15121/1897036
Faulds, James, Brown, Stephen, Smith, Connor, Queen, John, and Treitel, Sven. 2021. "Machine Learning Model Geotiffs - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada". United States. https://dx.doi.org/10.15121/1897036. https://gdr.openei.org/submissions/1351.
@div{oedi_5794, title = {Machine Learning Model Geotiffs - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada}, author = {Faulds, James, Brown, Stephen, Smith, Connor, Queen, John, and Treitel, Sven.}, abstractNote = {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.}, doi = {10.15121/1897036}, url = {https://gdr.openei.org/submissions/1351}, journal = {}, number = , volume = , place = {United States}, year = {2021}, month = {06}}
https://dx.doi.org/10.15121/1897036
Details
Data from Jun 1, 2021
Last updated Nov 7, 2022
Submitted Aug 26, 2022
Organization
Nevada Bureau of Mines and Geology
Contact
Elijah Mlawsky
775.682.9010
Authors
Original Source
https://gdr.openei.org/submissions/1351Research Areas
Keywords
geothermal, energy, Neural Network, Bayesian, ANN, ELM, BNN, Principal Component, PCA, NMF, Machine Learning, Algorithm, Play Fairway, Nevada, PFA, Great Basin, geotiff, exploration, characterization, inputs, outputs, raster, feature set, training sitesDOE 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