Brady Geodatabase for Geothermal Exploration Artificial Intelligence
These files contain the geodatabases related to Brady's Geothermal Field. It includes all input and output files for the Geothermal Exploration Artificial Intelligence. Input and output files are sorted into three categories: raw data, pre-processed data, and analysis (post-processed data). In each of these categories there are six additional types of raster catalogs which are titled Radar, SWIR, Thermal, Geophysics, Geology, and Wells. These inputs and outputs were used with the Geothermal Exploration Artificial Intelligence to identify indicators of blind geothermal systems at the Brady Hot Springs Geothermal Site. The included zip file is a geodatabase to be used with ArcGIS and the tar file is an inclusive database that encompasses the inputs and outputs for the Brady Hot Springs Geothermal Site.
Citation Formats
Colorado School of Mines. (2021). Brady Geodatabase for Geothermal Exploration Artificial Intelligence [data set]. Retrieved from https://dx.doi.org/10.15121/1797281.
Moraga, Jim, Cavur, Mahmut, Duzgun, H. Sebnem, Soydan, Hilal, and Jin, Ge. Brady Geodatabase for Geothermal Exploration Artificial Intelligence. United States: N.p., 27 Apr, 2021. Web. doi: 10.15121/1797281.
Moraga, Jim, Cavur, Mahmut, Duzgun, H. Sebnem, Soydan, Hilal, & Jin, Ge. Brady Geodatabase for Geothermal Exploration Artificial Intelligence. United States. https://dx.doi.org/10.15121/1797281
Moraga, Jim, Cavur, Mahmut, Duzgun, H. Sebnem, Soydan, Hilal, and Jin, Ge. 2021. "Brady Geodatabase for Geothermal Exploration Artificial Intelligence". United States. https://dx.doi.org/10.15121/1797281. https://gdr.openei.org/submissions/1304.
@div{oedi_4089, title = {Brady Geodatabase for Geothermal Exploration Artificial Intelligence}, author = {Moraga, Jim, Cavur, Mahmut, Duzgun, H. Sebnem, Soydan, Hilal, and Jin, Ge.}, abstractNote = {These files contain the geodatabases related to Brady's Geothermal Field. It includes all input and output files for the Geothermal Exploration Artificial Intelligence. Input and output files are sorted into three categories: raw data, pre-processed data, and analysis (post-processed data). In each of these categories there are six additional types of raster catalogs which are titled Radar, SWIR, Thermal, Geophysics, Geology, and Wells. These inputs and outputs were used with the Geothermal Exploration Artificial Intelligence to identify indicators of blind geothermal systems at the Brady Hot Springs Geothermal Site. The included zip file is a geodatabase to be used with ArcGIS and the tar file is an inclusive database that encompasses the inputs and outputs for the Brady Hot Springs Geothermal Site.}, doi = {10.15121/1797281}, url = {https://gdr.openei.org/submissions/1304}, journal = {}, number = , volume = , place = {United States}, year = {2021}, month = {04}}
https://dx.doi.org/10.15121/1797281
Details
Data from Apr 27, 2021
Last updated Sep 7, 2021
Submitted Apr 28, 2021
Organization
Colorado School of Mines
Contact
Jim Moraga
303.273.3768
Authors
Original Source
https://gdr.openei.org/submissions/1304Research Areas
Keywords
geothermal, energy, geodatabase, Brady hot springs, Brady, artificial intelligence, AI, Brady Well, seismic, remote sensing, hyperspectral, geospatial database, deep learning, machine learning, exploration, site detection, geothermal site detection, anomaly detection, short wavelength infrared, SWIR, support vector machine, SVM, land surface temperature, LST, well, raw data, processed data, Nevada, ArcGIS, model, database, hydrothermal, geophysics, radar, GIS, blind, blind system, deformation, geophysical, hyperspectral imaging, conceptual model, fault, preprocessed, raster, vector, field data, geospatial dataDOE Project Details
Project Name Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning
Project Lead Mike Weathers
Project Number EE0008760
