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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

Colorado School of Mines. (2021). Programs and Code for Geothermal Exploration Artificial Intelligence [data set]. Retrieved from https://dx.doi.org/10.15121/1787330.
Export Citation to RIS
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://data.openei.org/submissions/4080.
@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://data.openei.org/submissions/4080}, journal = {}, number = , volume = , place = {United States}, year = {2021}, month = {04}}
- 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://data.openei.org/submissions/4080}, journal = {}, number = , volume = , place = {United States}, year = {2021}, month = {04}}" readonly />
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

Research Areas

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