Programs and Code for Geothermal Exploration Artificial Intelligence
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
Colorado School of Mines. (2021). Programs and Code for Geothermal Exploration Artificial Intelligence [data set]. Retrieved from https://dx.doi.org/10.15121/1787330.
Moraga, Jim. Programs and Code for Geothermal Exploration Artificial Intelligence. United States: N.p., 27 Apr, 2021. Web. doi: 10.15121/1787330.
Moraga, Jim. Programs and Code for Geothermal Exploration Artificial Intelligence. United States. https://dx.doi.org/10.15121/1787330
Moraga, Jim. 2021. "Programs and Code for Geothermal Exploration Artificial Intelligence". United States. https://dx.doi.org/10.15121/1787330. https://gdr.openei.org/submissions/1307.
@div{oedi_4080, 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
}, doi = {10.15121/1787330}, url = {https://gdr.openei.org/submissions/1307}, journal = {}, number = , volume = , place = {United States}, year = {2021}, month = {04}}
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
Original Source
https://gdr.openei.org/submissions/1307Research Areas
Keywords
geothermal, energy, code, R, Shell scripts, Geothermal AI, Machine Learning, Self Organizing Map, K-Means, Python, AI, artificial intelligence, deep learning, exploration, geothermal exploration, remote sensing, blind, site detection, LST, land surface temperature, NumPy, raster, TensorFlow, k mean, anomaly detection, Landsat ADR LST, sbatch, SLURM, ShellDOE Project Details
Project Name Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning
Project Lead Mike Weathers
Project Number EE0008760
