Programs and Code for Subsurface and MultiSite Geothermal Exploration Artificial Intelligence

Awaiting release License 

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 -
Export Citation to RIS
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: 2025-04-24} }

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

Ebubekir Demir

Colorado School of Mines

Sebnem Duzgun

Colorado School of Mines

DOE Project Details

Project Name Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning

Project Lead Mike Weathers

Project Number EE0008760

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