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GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources

Metadata Updated: June 11, 2023

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.

Access & Use Information

Public: This dataset is intended for public access and use. License: Creative Commons Attribution

Downloads & Resources


Metadata Created Date May 27, 2022
Metadata Updated Date June 11, 2023

Metadata Source

Harvested from OpenEI data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date May 27, 2022
Metadata Updated Date June 11, 2023
Publisher Stanford University
Doi 10.15121/1869828
Data First Published 2022-04-04T06:00:00Z
Data Last Modified 2022-05-26T16:04:57Z
Public Access Level public
Bureau Code 019:20
Metadata Context
Metadata Catalog ID
Schema Version
Catalog Describedby
Data Quality True
Harvest Object Id 526cc2b8-bc08-4c0d-802c-566686428d6c
Harvest Source Id 7cbf9085-0290-4e9f-bec1-91653baeddfd
Harvest Source Title OpenEI data.json
Homepage URL
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Program Code 019:006
Projectlead Mike Weathers
Projectnumber 35514
Projecttitle Thermo-hydro-chemical data for machine learning model development
Source Datajson Identifier True
Source Hash acabd7d4d796ac3df97065d3b3b2b8c7a749b381
Source Schema Version 1.1
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