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
TY - DATA
AB - 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.
AU - Demir, Ebubekir
A2 - Duzgun, Sebnem
DB - Geothermal Data Repository
DP - Open EI | National Renewable Energy Laboratory
DO -
KW - geothermal
KW - energy
KW - geothermal exploration
KW - subsurface
KW - surface
KW - multi-site
KW - artificial intelligence
KW - AI
KW - machine learning
KW - ML
KW - deep learning
KW - hyperspectral imaging
KW - 3D modeling
KW - 2D classification
KW - Voxel data
KW - raster data
KW - land surface temperature
KW - mineral markers
KW - K-means
KW - data processing
KW - TensorFlow
KW - Python
KW - code
KW - GPU
KW - exploration
LA - English
DA - 2023/09/01
PY - 2023
PB - Colorado School of Mines
T1 - Programs and Code for Subsurface and MultiSite Geothermal Exploration Artificial Intelligence
UR - https://gdr.openei.org/submissions/1694
ER -
Demir, Ebubekir, and Sebnem Duzgun. Programs and Code for Subsurface and MultiSite Geothermal Exploration Artificial Intelligence. Colorado School of Mines, 1 September, 2023, Geothermal Data Repository. https://gdr.openei.org/submissions/1694.
Demir, E., & Duzgun, S. (2023). Programs and Code for Subsurface and MultiSite Geothermal Exploration Artificial Intelligence. [Data set]. Geothermal Data Repository. Colorado School of Mines. https://gdr.openei.org/submissions/1694
Demir, Ebubekir and Sebnem Duzgun. Programs and Code for Subsurface and MultiSite Geothermal Exploration Artificial Intelligence. Colorado School of Mines, September, 1, 2023. Distributed by Geothermal Data Repository. https://gdr.openei.org/submissions/1694
@misc{GDR_Dataset_1694,
title = {Programs and Code for Subsurface and MultiSite Geothermal Exploration Artificial Intelligence},
author = {Demir, Ebubekir and 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.},
url = {https://gdr.openei.org/submissions/1694},
year = {2023},
howpublished = {Geothermal Data Repository, Colorado School of Mines, https://gdr.openei.org/submissions/1694},
note = {Accessed: 2025-04-25}
}
Details
Data from Sep 1, 2023
Last updated Nov 23, 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
