Programs and Code for Geothermal Exploration Artificial Intelligence

Dataset · Tue Apr 27 00:00:00 EDT 2021

DOI:https://doi.org/10.15121/1787330· OSTI ID:1787330

Moraga, Jim ^[1]

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1. Colorado School of Mines

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

• Research Organization: DOE Geothermal Data Repository; Colorado School of Mines
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Program (EE-4G)
• Contributing Organization: Colorado School of Mines
• DOE Contract Number: EE0008760
• OSTI ID: 1787330
• Report Number(s): 1307
• Availability: [e-mail]
• Country of Publication: United States
• Language: English

Related Subjects

15 GEOTHERMAL ENERGY
AI
Geothermal AI
K-Means
LST
Landsat ADR LST
Machine Learning
NumPy
Python
R
SLURM
Self Organizing Map
Shell
Shell scripts
TensorFlow
anomaly detection
artificial intelligence
blind
code
deep learning
energy
exploration
geothermal
geothermal exploration
k mean
land surface temperature
raster
remote sensing
sbatch
site detection