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
TY - DATA
AB - 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.
AU - Vesselinov, Velimir
DB - Geothermal Data Repository
DP - Open EI | National Renewable Energy Laboratory
DO - 10.15121/1773700
KW - geothermal
KW - energy
KW - machine-learning
KW - New Mexico
KW - Brady
KW - Nevada
KW - Great Basin
KW - Southwest New Mexico
KW - multi-physics
KW - Brady Hot Springs
KW - SmartTensors
KW - GeoThermalCloud
KW - geothermal cloud
KW - Los Alamos National Laboratory
KW - site data
KW - simulation
KW - machine learning
KW - model
LA - English
DA - 2021/03/29
PY - 2021
PB - Los Alamos National Laboratory
T1 - GeoThermalCloud framework for fusion of big data and multi-physics models in Nevada and Southwest New Mexico
UR - https://doi.org/10.15121/1773700
ER -
Vesselinov, Velimir. GeoThermalCloud framework for fusion of big data and multi-physics models in Nevada and Southwest New Mexico. Los Alamos National Laboratory, 29 March, 2021, Geothermal Data Repository. https://doi.org/10.15121/1773700.
Vesselinov, V. (2021). GeoThermalCloud framework for fusion of big data and multi-physics models in Nevada and Southwest New Mexico. [Data set]. Geothermal Data Repository. Los Alamos National Laboratory. https://doi.org/10.15121/1773700
Vesselinov, Velimir. GeoThermalCloud framework for fusion of big data and multi-physics models in Nevada and Southwest New Mexico. Los Alamos National Laboratory, March, 29, 2021. Distributed by Geothermal Data Repository. https://doi.org/10.15121/1773700
@misc{GDR_Dataset_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.},
url = {https://gdr.openei.org/submissions/1297},
year = {2021},
howpublished = {Geothermal Data Repository, Los Alamos National Laboratory, https://doi.org/10.15121/1773700},
note = {Accessed: 2025-04-27},
doi = {10.15121/1773700}
}
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
