GeoThermalCloud framework for fusion of big data and multi-physics models in Nevada and Southwest New Mexico
Our GeoThermalCloud framework is designed to process geothermal datasets using a novel toolbox for unsupervised and physics-informed machine learning called SmartTensors. More information about GeoThermalCloud can be found at the GeoThermalCloud GitHub Repository. More information about SmartTensors can be found at the SmartTensors Github Repository and the SmartTensors page at LANL.gov. Links to these pages are included in this submission.
GeoThermalCloud.jl is a repository containing all the data and codes required to demonstrate applications of machine learning methods for geothermal exploration.
GeoThermalCloud.jl includes:
- site data
- simulation scripts
- jupyter notebooks
- intermediate results
- code outputs
- summary figures
- readme markdown files
GeoThermalCloud.jl showcases the machine learning analyses performed for the following geothermal sites:
- Brady: geothermal exploration of the Brady geothermal site, Nevada
- SWNM: geothermal exploration of the Southwest New Mexico (SWNM) region
- GreatBasin: geothermal exploration of the Great Basin region, Nevada
Reports, research papers, and presentations summarizing these machine learning analyses are also available and will be posted soon.
Citation Formats
Los Alamos National Laboratory. (2021). GeoThermalCloud framework for fusion of big data and multi-physics models in Nevada and Southwest New Mexico [data set]. Retrieved from https://dx.doi.org/10.15121/1773700.
Vesselinov, Velimir. GeoThermalCloud framework for fusion of big data and multi-physics models in Nevada and Southwest New Mexico. United States: N.p., 29 Mar, 2021. Web. doi: 10.15121/1773700.
Vesselinov, Velimir. GeoThermalCloud framework for fusion of big data and multi-physics models in Nevada and Southwest New Mexico. United States. https://dx.doi.org/10.15121/1773700
Vesselinov, Velimir. 2021. "GeoThermalCloud framework for fusion of big data and multi-physics models in Nevada and Southwest New Mexico". United States. https://dx.doi.org/10.15121/1773700. https://gdr.openei.org/submissions/1297.
@div{oedi_1297, title = {GeoThermalCloud framework for fusion of big data and multi-physics models in Nevada and Southwest New Mexico}, author = {Vesselinov, Velimir.}, abstractNote = {Our GeoThermalCloud framework is designed to process geothermal datasets using a novel toolbox for unsupervised and physics-informed machine learning called SmartTensors. More information about GeoThermalCloud can be found at the GeoThermalCloud GitHub Repository. More information about SmartTensors can be found at the SmartTensors Github Repository and the SmartTensors page at LANL.gov. Links to these pages are included in this submission.
GeoThermalCloud.jl is a repository containing all the data and codes required to demonstrate applications of machine learning methods for geothermal exploration.
GeoThermalCloud.jl includes:
- site data
- simulation scripts
- jupyter notebooks
- intermediate results
- code outputs
- summary figures
- readme markdown files
GeoThermalCloud.jl showcases the machine learning analyses performed for the following geothermal sites:
- Brady: geothermal exploration of the Brady geothermal site, Nevada
- SWNM: geothermal exploration of the Southwest New Mexico (SWNM) region
- GreatBasin: geothermal exploration of the Great Basin region, Nevada
Reports, research papers, and presentations summarizing these machine learning analyses are also available and will be posted soon.}, doi = {10.15121/1773700}, url = {https://gdr.openei.org/submissions/1297}, journal = {}, number = , volume = , place = {United States}, year = {2021}, month = {03}}
https://dx.doi.org/10.15121/1773700
Details
Data from Mar 29, 2021
Last updated May 17, 2021
Submitted Mar 29, 2021
Organization
Los Alamos National Laboratory
Contact
Velimir Vesselinov
505.412.7159
Authors
Keywords
geothermal, energy, machine-learning, New Mexico, Brady, Nevada, Great Basin, Southwest New Mexico, multi-physics, Brady Hot Springs, SmartTensors, GeoThermalCloud, geothermal cloud, Los Alamos National Laboratory, site data, simulation, machine learning, modelDOE Project Details
Project Name Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources
Project Lead Mike Weathers
Project Number FY19 AOP 3.1.8.7
