Return to search results
GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources
Geothermal exploration and production are challenging, expensive and risky. The GeoThermalCloud uses Machine Learning to predict the location of hidden geothermal resources. This submission includes a training dataset for the GeoThermalCloud neural network. Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources.
Resources
3 resources available
-
AR Training Dataset 3D_diff.hdf5
HDF5 -
Parameters Documentation.txt
TXT -
GeoThermalCloud GitHub
JL
Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[
"019:20"
]
|
| contactPoint |
{
"fn": "Dimitrios Ioannis Belivanis",
"@type": "vcard:Contact",
"hasEmail": "mailto:dbelivan@stanford.edu"
}
|
| dataQuality |
true
|
| description | Geothermal exploration and production are challenging, expensive and risky. The GeoThermalCloud uses Machine Learning to predict the location of hidden geothermal resources. This submission includes a training dataset for the GeoThermalCloud neural network. Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources. |
| distribution |
[
{
"@type": "dcat:Distribution",
"title": "AR Training Dataset 3D_diff.hdf5",
"format": "hdf5",
"accessURL": "https://gdr.openei.org/files/1377/AR_dataset_3D_diff.hdf5",
"mediaType": "application/octet-stream",
"description": "Dataset of type HDF5 for training NN (neural network). The Parameters Documentation resource in this submission outlines the input and output parameters of the dataset."
},
{
"@type": "dcat:Distribution",
"title": "Parameters Documentation.txt",
"format": "txt",
"accessURL": "https://gdr.openei.org/files/1377/ParametersDocumentation.txt",
"mediaType": "text/plain",
"description": "This documentation explains the organization of the AR Training Dataset for the GeoThermalCloud Neural Network. Parameters detailed include the input and output parameters."
},
{
"@type": "dcat:Distribution",
"title": "GeoThermalCloud GitHub",
"format": "jl",
"accessURL": "https://github.com/SmartTensors/GeoThermalCloud.jl",
"mediaType": "application/octet-stream",
"description": "Geothermal Cloud for Machine Learning. Includes the code used in the GeoThermalCloud Project."
}
]
|
| DOI | 10.15121/1869828 |
| identifier | https://data.openei.org/submissions/7488 |
| issued | 2022-04-04T06:00:00Z |
| keyword |
[
"AI",
"artificial intelligence",
"development",
"discovery",
"energy",
"exploration",
"geothermal",
"hidden geothermal resources",
"machine learning",
"model",
"modeling",
"neural network",
"processed data",
"remote sensing",
"resource",
"resource detection",
"training data",
"training dataset"
]
|
| landingPage | https://gdr.openei.org/submissions/1377 |
| license | https://creativecommons.org/licenses/by/4.0/ |
| modified | 2022-05-26T16:04:57Z |
| programCode |
[
"019:006"
]
|
| projectLead | Mike Weathers |
| projectNumber |
"35514"
|
| projectTitle | Thermo-hydro-chemical data for machine learning model development |
| publisher |
{
"name": "Stanford University",
"@type": "org:Organization"
}
|
| spatial |
"{"type":"Polygon","coordinates":[[[-106.3249889031506,32.67710958643174],[-106.3249889031506,32.67710958643174],[-106.3249889031506,32.67710958643174],[-106.3249889031506,32.67710958643174],[-106.3249889031506,32.67710958643174]]]}"
|
| title | GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources |
