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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.
Resources
4 resources available
-
GeoThermalCloud.zip
ZIP -
SmartTensors GitHub Repository
HTML -
GeoThermalCloud GitHub Repository
JL -
SmartTensors Webpage LANL
HTML
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[ "019:20" ] |
| contactPoint |
{ "fn": "Velimir Vesselinov", "@type": "vcard:Contact", "hasEmail": "mailto:vvv@lanl.gov" } |
| dataQuality |
true
|
| description | 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. |
| distribution |
[ { "@type": "dcat:Distribution", "title": "GeoThermalCloud.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/1297/GeoThermalCloud.zip", "mediaType": "application/zip", "description": "A complete archive of our GeoThermalCloud framework. The archive includes scripts and datasets to solve a series of geothermal problems using machine learning. A README is included in the archive." }, { "@type": "dcat:Distribution", "title": "SmartTensors GitHub Repository", "format": "HTML", "accessURL": "https://github.com/SmartTensors", "mediaType": "text/html", "description": "SmartTensors repository for machine learning hosted on GitHub" }, { "@type": "dcat:Distribution", "title": "GeoThermalCloud GitHub Repository", "format": "jl", "accessURL": "https://github.com/SmartTensors/GeoThermalCloud.jl", "mediaType": "application/octet-stream", "description": "GeoThermalCloud repository for machine learning hosted on GitHub" }, { "@type": "dcat:Distribution", "title": "SmartTensors Webpage LANL", "format": "HTML", "accessURL": "https://tensors.lanl.gov/", "mediaType": "text/html", "description": "SmartTensors for Machine Learning hosted on the Los Alamos National Laboratory (LANL) website" } ] |
| DOI | 10.15121/1773700 |
| identifier | https://data.openei.org/submissions/7415 |
| issued | 2021-03-29T06:00:00Z |
| keyword |
[ "Brady", "Brady Hot Springs", "GeoThermalCloud", "Great Basin", "Los Alamos National Laboratory", "Nevada", "New Mexico", "SmartTensors", "Southwest New Mexico", "energy", "geothermal", "geothermal cloud", "machine learning", "machine-learning", "model", "multi-physics", "simulation", "site data" ] |
| landingPage | https://gdr.openei.org/submissions/1297 |
| license | https://creativecommons.org/licenses/by/4.0/ |
| modified | 2021-05-17T15:57:18Z |
| programCode |
[ "019:006" ] |
| projectLead | Mike Weathers |
| projectNumber | FY19 AOP 3.1.8.7 |
| projectTitle | Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources |
| publisher |
{ "name": "Los Alamos National Laboratory", "@type": "org:Organization" } |
| spatial |
"{"type":"Polygon","coordinates":[[[-121.4523484375,32.45439109625768],[-109.9497640625,32.45439109625768],[-109.9497640625,43.82997417540995],[-121.4523484375,43.82997417540995],[-121.4523484375,32.45439109625768]]]}"
|
| title | GeoThermalCloud framework for fusion of big data and multi-physics models in Nevada and Southwest New Mexico |
