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
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 -
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
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