Programs and Code for Geothermal Exploration Artificial Intelligence

Publicly accessible License 

The scripts below are used to run the Geothermal Exploration Artificial Intelligence developed within the "Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning" project. It includes all scripts for pre-processing and processing, including:
- Land Surface Temperature K-Means classifier
- Labeling AI using Self Organizing Maps (SOM)
- Post-processing for Permanent Scatterer InSAR (PSInSAR) analysis with SOM
- Mineral marker summarizing
- Artificial Intelligence (AI) Data splitting: creates data set from a single raster file
- Artificial Intelligence Model: creates AI from a single data set, after splitting in Train, Validation and Test subsets
- AI Mapper: creates a classification map based on a raster file

Citation Formats

TY - DATA AB - The scripts below are used to run the Geothermal Exploration Artificial Intelligence developed within the "Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning" project. It includes all scripts for pre-processing and processing, including: - Land Surface Temperature K-Means classifier - Labeling AI using Self Organizing Maps (SOM) - Post-processing for Permanent Scatterer InSAR (PSInSAR) analysis with SOM - Mineral marker summarizing - Artificial Intelligence (AI) Data splitting: creates data set from a single raster file - Artificial Intelligence Model: creates AI from a single data set, after splitting in Train, Validation and Test subsets - AI Mapper: creates a classification map based on a raster file AU - Moraga, Jim DB - Geothermal Data Repository DP - Open EI | National Renewable Energy Laboratory DO - 10.15121/1787330 KW - geothermal KW - energy KW - code KW - R KW - Shell scripts KW - Geothermal AI KW - Machine Learning KW - Self Organizing Map KW - K-Means KW - Python KW - AI KW - artificial intelligence KW - deep learning KW - exploration KW - geothermal exploration KW - remote sensing KW - blind KW - site detection KW - LST KW - land surface temperature KW - NumPy KW - raster KW - TensorFlow KW - k mean KW - anomaly detection KW - Landsat ADR LST KW - sbatch KW - SLURM KW - Shell LA - English DA - 2021/04/27 PY - 2021 PB - Colorado School of Mines T1 - Programs and Code for Geothermal Exploration Artificial Intelligence UR - https://doi.org/10.15121/1787330 ER -
Export Citation to RIS
Moraga, Jim. Programs and Code for Geothermal Exploration Artificial Intelligence. Colorado School of Mines, 27 April, 2021, Geothermal Data Repository. https://doi.org/10.15121/1787330.
Moraga, J. (2021). Programs and Code for Geothermal Exploration Artificial Intelligence. [Data set]. Geothermal Data Repository. Colorado School of Mines. https://doi.org/10.15121/1787330
Moraga, Jim. Programs and Code for Geothermal Exploration Artificial Intelligence. Colorado School of Mines, April, 27, 2021. Distributed by Geothermal Data Repository. https://doi.org/10.15121/1787330
@misc{GDR_Dataset_1307, title = {Programs and Code for Geothermal Exploration Artificial Intelligence}, author = {Moraga, Jim}, abstractNote = {The scripts below are used to run the Geothermal Exploration Artificial Intelligence developed within the "Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning" project. It includes all scripts for pre-processing and processing, including:
- Land Surface Temperature K-Means classifier
- Labeling AI using Self Organizing Maps (SOM)
- Post-processing for Permanent Scatterer InSAR (PSInSAR) analysis with SOM
- Mineral marker summarizing
- Artificial Intelligence (AI) Data splitting: creates data set from a single raster file
- Artificial Intelligence Model: creates AI from a single data set, after splitting in Train, Validation and Test subsets
- AI Mapper: creates a classification map based on a raster file
}, url = {https://gdr.openei.org/submissions/1307}, year = {2021}, howpublished = {Geothermal Data Repository, Colorado School of Mines, https://doi.org/10.15121/1787330}, note = {Accessed: 2025-05-03}, doi = {10.15121/1787330} }
https://dx.doi.org/10.15121/1787330

Details

Data from Apr 27, 2021

Last updated Jun 9, 2021

Submitted Apr 28, 2021

Organization

Colorado School of Mines

Contact

Jim Moraga

303.273.3768

Authors

Jim Moraga

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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