Related Datasets

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

DOI 10.15121/1869828

Publicly accessible License

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.

Download All Resources 1.04 GB — 2 files

AR Training Dataset 3D_diff.hdf5

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.

Download 1.04 GB

GeoThermalCloud GitHub

Jupyter Notebook HTML

Geothermal Cloud for Machine Learning. Includes the code used in the GeoThermalCloud Project.

View Repository

Parameters Documentation.txt

This documentation explains the organization of the AR Training Dataset for the GeoThermalCloud Neural Network. Parameters detailed include the input and output paramet... more

Download 0.95 kB

Citation Formats

Stanford University. (2022). GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources [data set].  Retrieved from https://dx.doi.org/10.15121/1869828.

Export Citation to RIS

Ahmmed, Bulbul. GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources. United States: N.p., 04 Apr, 2022. Web. doi: 10.15121/1869828.

Ahmmed, Bulbul. GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources. United States. https://dx.doi.org/10.15121/1869828

Ahmmed, Bulbul. 2022. "GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources". United States. https://dx.doi.org/10.15121/1869828. https://gdr.openei.org/submissions/1377.

@div{oedi_1377, title = {GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources}, author = {Ahmmed, Bulbul.}, abstractNote = {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.}, doi = {10.15121/1869828}, url = {https://gdr.openei.org/submissions/1377}, journal = {}, number = , volume = , place = {United States}, year = {2022}, month = {04}}

https://dx.doi.org/10.15121/1869828

Details

Data from Apr 4, 2022

Last updated May 26, 2022

Submitted Apr 25, 2022

Organization

Stanford University

Contact

Dimitrios Ioannis Belivanis

302.635.4690

Authors

Bulbul Ahmmed

Los Alamos National Laboratory

Keywords

geothermal, energy, machine learning, artificial intelligence, AI, exploration, model, modeling, processed data, training data, training dataset, remote sensing, hidden geothermal resources, resource detection, discovery, development, resource, neural network

DOE Project Details

Project Name Thermo-hydro-chemical data for machine learning model development

Project Lead Mike Weathers

Project Number 35514