Machine Learning Model Geotiffs - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

DOI 10.15121/1897036
Publicly accessible 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, 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

TY - DATA AB - 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. AU - Faulds, James A2 - Brown, Stephen A3 - Smith, Connor A4 - Queen, John A5 - Treitel, Sven DB - Geothermal Data Repository DP - Open EI | National Renewable Energy Laboratory DO - 10.15121/1897036 KW - geothermal KW - energy KW - Neural Network KW - Bayesian KW - ANN KW - ELM KW - BNN KW - Principal Component KW - PCA KW - NMF KW - Machine Learning KW - Algorithm KW - Play Fairway KW - Nevada KW - PFA KW - Great Basin KW - geotiff KW - exploration KW - characterization KW - inputs KW - outputs KW - raster KW - feature set KW - training sites LA - English DA - 2021/06/01 PY - 2021 PB - Nevada Bureau of Mines and Geology T1 - Machine Learning Model Geotiffs - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada UR - https://doi.org/10.15121/1897036 ER -
Export Citation to RIS
Faulds, James, et al. Machine Learning Model Geotiffs - 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, Geothermal Data Repository. https://doi.org/10.15121/1897036.
Faulds, J., Brown, S., Smith, C., Queen, J., & Treitel, S. (2021). Machine Learning Model Geotiffs - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada. [Data set]. Geothermal Data Repository. Nevada Bureau of Mines and Geology. https://doi.org/10.15121/1897036
Faulds, James, Stephen Brown, Connor Smith, John Queen, and Sven Treitel. Machine Learning Model Geotiffs - 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 Geothermal Data Repository. https://doi.org/10.15121/1897036
@misc{GDR_Dataset_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 and Brown, Stephen and Smith, Connor and 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.}, url = {https://gdr.openei.org/submissions/1351}, year = {2021}, howpublished = {Geothermal Data Repository, Nevada Bureau of Mines and Geology, https://doi.org/10.15121/1897036}, note = {Accessed: 2025-04-22}, doi = {10.15121/1897036} }
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

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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