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.
OwnerNational Renewable Energy Laboratory (NREL) - view all
Update frequencyunknown
Last updatedabout 1 year ago
OverviewBradyBrady Hot SpringsGeoThermalCloudGreat BasinLos Alamos National LaboratoryNevadaNew MexicoSmartTensorsSouthwest New Mexicoenergygeothermalgeothermal cloudmachine learningmachine-learningmodelmulti-physicssimulationsite data
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dcat_issued2021-03-29T06:00:00Z
dcat_modified2021-05-17T15:57:18Z
dcat_publisher_nameLos Alamos National Laboratory
guidhttps://data.openei.org/submissions/4059
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