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
TY - DATA
AB - 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.
AU - Moraga, Jim
A2 - Cavur, Mahmut
A3 - Duzgun, H. Sebnem
A4 - Soydan, Hilal
A5 - Jin, Ge
DB - Geothermal Data Repository
DP - Open EI | National Renewable Energy Laboratory
DO - 10.15121/1797281
KW - geothermal
KW - energy
KW - geodatabase
KW - Brady hot springs
KW - Brady
KW - artificial intelligence
KW - AI
KW - Brady Well
KW - seismic
KW - remote sensing
KW - hyperspectral
KW - geospatial database
KW - deep learning
KW - machine learning
KW - exploration
KW - site detection
KW - geothermal site detection
KW - anomaly detection
KW - short wavelength infrared
KW - SWIR
KW - support vector machine
KW - SVM
KW - land surface temperature
KW - LST
KW - well
KW - raw data
KW - processed data
KW - Nevada
KW - ArcGIS
KW - model
KW - database
KW - hydrothermal
KW - geophysics
KW - radar
KW - GIS
KW - blind
KW - blind system
KW - deformation
KW - geophysical
KW - hyperspectral imaging
KW - conceptual model
KW - fault
KW - preprocessed
KW - raster
KW - vector
KW - field data
KW - geospatial data
LA - English
DA - 2021/04/27
PY - 2021
PB - Colorado School of Mines
T1 - Brady Geodatabase for Geothermal Exploration Artificial Intelligence
UR - https://doi.org/10.15121/1797281
ER -
Moraga, Jim, et al. Brady Geodatabase for Geothermal Exploration Artificial Intelligence. Colorado School of Mines, 27 April, 2021, Geothermal Data Repository. https://doi.org/10.15121/1797281.
Moraga, J., Cavur, M., Duzgun, H., Soydan, H., & Jin, G. (2021). Brady Geodatabase for Geothermal Exploration Artificial Intelligence. [Data set]. Geothermal Data Repository. Colorado School of Mines. https://doi.org/10.15121/1797281
Moraga, Jim, Mahmut Cavur, H. Sebnem Duzgun, Hilal Soydan, and Ge Jin. Brady Geodatabase for Geothermal Exploration Artificial Intelligence. Colorado School of Mines, April, 27, 2021. Distributed by Geothermal Data Repository. https://doi.org/10.15121/1797281
@misc{GDR_Dataset_1304,
title = {Brady Geodatabase for Geothermal Exploration Artificial Intelligence},
author = {Moraga, Jim and Cavur, Mahmut and Duzgun, H. Sebnem and 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.},
url = {https://gdr.openei.org/submissions/1304},
year = {2021},
howpublished = {Geothermal Data Repository, Colorado School of Mines, https://doi.org/10.15121/1797281},
note = {Accessed: 2025-05-28},
doi = {10.15121/1797281}
}
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
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
