Related Datasets

Programs and Code for Geothermal Exploration Artificial Intelligence

DOI 10.15121/1787330

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

Download All Resources 710.72 kB — 11 files

Create DOE Dataset.py

Python NumPy

Creates the data set for the Geo AI. More information of this code can be found in the README within this submission.

Download 3.03 kB

DOE ANN Map.py

Python

Maps the classification from a raster image using a trained AI model. Requires Python 3 and multiple Python software packages such as NumPy and OSGeo.

Download 9.73 kB

DOE ANN.zip

Python and Shell (SLURM) scripts to create a dataset, build an AI and map using the created AI. It requires Python 3, TensorFlow 2.4, and a machine with one or multiple... more

Download 640 kB

DOE GeoAI.py

Python

Creates a Geothermal AI model from labeled data. This is the main program to create, train and use an ANN to classify regions based on geothermal potential. Requires Py... more

179

Download 19.78 kB

DOE TIFF.zip

Libraries to be used with the Geothermal AI and related python scripts.

Download 6.08 kB

Displacement SOM R Scripts.zip

Post-processing for PSInSAR analysis with SOM R scripts that use the CVS output from Sarproz to generate SOM-ready data (by cropping data and recalculating slopes), and... more

Download 8.61 kB

LST Extract.sh

Bash

Shell script to extract relevant files from a directory with Landsat 8 ADR LST compressed files.

Download 1.8 kB

LST R Scripts.zip

Land Surface Temperature K-Means Classifier R scripts to extract data, classify through K-Means and save final result for Land Surface Temperature anomalies (hot areas... more

Download 4.48 kB

Mineral Markers.zip

Mineral marker summarizing R scripts to summarize the outputs from ENVI target detection for further data fusion. Outputs the results of CEM, SAM, ACE, MF, MTMF, OSP, T... more

Download 4.95 kB

README.md

README for the Geothermal AI. Provides a guide on how to properly run the provided code in this submission.

Download 1.99 kB

Sbatch Scripts.zip

Compressed directory with scripts to to be used to run the Geothermal AI and related python scripts in an HPC environment with SLURM.

Download 10.27 kB

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: 2025-05-23},
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