Machine learning model geotiffs - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

Awaiting curation License 

This submission contains geotiffs, supporting shapefiles and readmes for the inputs and output models of algorithms explored in the Nevada Geothermal Machine Learning project (DOE #EE0008762), 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://gdr.openei.org/submissions/1351.
Export Citation to RIS
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. https://gdr.openei.org/submissions/1351.
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://gdr.openei.org/submissions/1351
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://gdr.openei.org/submissions/1351.
@div{oedi_1351, 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 (DOE #EE0008762), 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 = {}, url = {https://gdr.openei.org/submissions/1351}, journal = {}, number = , volume = , place = {United States}, year = {2021}, month = {06}}

Details

Data from Jun 1, 2021

Last updated Aug 26, 2022

Submitted Aug 26, 2022

Organization

Nevada Bureau of Mines and Geology

Contact

Elijah Mlawsky

775.682.9010

Authors

James Faulds

Nevada Bureau of Mines and Geology

Stephen Brown

Massachusetts Institute of Technology

Connor Smith

Nevada Bureau of Mines and Geology

John Queen

Hi-Q Geophysical Inc.

Sven Treitel

Hi-Q Geophysical Inc.

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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