Programs and Code for Subsurface and MultiSite Geothermal Exploration Artificial Intelligence
This dataset provides Python scripts supporting both subsurface and surface geothermal exploration AI models developed for the project "Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning." It includes two main components: (1) scripts for subsurface geothermal exploration, which generate 3D models from input voxels to predict geothermal potential in subsurface regions, and (2) scripts for multisite surface exploration, utilizing land surface temperature and mineral markers to classify and map geothermal sites on the surface. The dataset covers all stages of processing, from data import and preprocessing through AI model training, testing, and validation, as well as final mapping of geothermal potential areas.
The subsurface exploration scripts generate a 3D geothermal model, while the multisite surface scripts support a 2D classification map from raster input. Requirements include Python 3, TensorFlow 2.4, and a machine with GPU support.
Citation Formats
Colorado School of Mines. (2023). Programs and Code for Subsurface and MultiSite Geothermal Exploration Artificial Intelligence [data set]. Retrieved from https://gdr.openei.org/submissions/1694.
Demir, Ebubekir, Duzgun, Sebnem. Programs and Code for Subsurface and MultiSite Geothermal Exploration Artificial Intelligence. United States: N.p., 01 Sep, 2023. Web. https://gdr.openei.org/submissions/1694.
Demir, Ebubekir, Duzgun, Sebnem. Programs and Code for Subsurface and MultiSite Geothermal Exploration Artificial Intelligence. United States. https://gdr.openei.org/submissions/1694
Demir, Ebubekir, Duzgun, Sebnem. 2023. "Programs and Code for Subsurface and MultiSite Geothermal Exploration Artificial Intelligence". United States. https://gdr.openei.org/submissions/1694.
@div{oedi_1694, title = {Programs and Code for Subsurface and MultiSite Geothermal Exploration Artificial Intelligence}, author = {Demir, Ebubekir, Duzgun, Sebnem.}, abstractNote = {This dataset provides Python scripts supporting both subsurface and surface geothermal exploration AI models developed for the project "Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning." It includes two main components: (1) scripts for subsurface geothermal exploration, which generate 3D models from input voxels to predict geothermal potential in subsurface regions, and (2) scripts for multisite surface exploration, utilizing land surface temperature and mineral markers to classify and map geothermal sites on the surface. The dataset covers all stages of processing, from data import and preprocessing through AI model training, testing, and validation, as well as final mapping of geothermal potential areas.
The subsurface exploration scripts generate a 3D geothermal model, while the multisite surface scripts support a 2D classification map from raster input. Requirements include Python 3, TensorFlow 2.4, and a machine with GPU support.}, doi = {}, url = {https://gdr.openei.org/submissions/1694}, journal = {}, number = , volume = , place = {United States}, year = {2023}, month = {09}}
Details
Data from Sep 1, 2023
Last updated Nov 15, 2024
Submitted Nov 11, 2024
Organization
Colorado School of Mines
Contact
Ebubekir Demir
303.273.3597
Authors
Keywords
geothermal, energy, geothermal exploration, subsurface, surface, multi-site, artificial intelligence, AI, machine learning, ML, deep learning, hyperspectral imaging, 3D modeling, 2D classification, Voxel data, raster data, land surface temperature, mineral markers, K-means, data processing, TensorFlow, Python, code, GPU, explorationDOE Project Details
Project Name Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning
Project Lead Mike Weathers
Project Number EE0008760