Desert Peak Geodatabase for Geothermal Exploration Artificial Intelligence

Abstract

These files contain the geodatabases related to the Desert Peak Geothermal Field. It includes all input and output files used in the project. The files include data categories of raw data, pre-processed data, and analysis (post-processed data). In each of these categories there are six additional types of raster catalogs including Radar, SWIR, Thermal, Geophysics, Geology, and Wells. The files for the Desert Peak Geothermal Site are used with the Geothermal Exploration Artificial Intelligence to identify indicators of blind geothermal systems. 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 Desert Peak Geothermal Field.

3 Resources

*downloads since 2019

Related Datasets

Datasets associated with the same DOE project
  Submission Name Resources Submitted Status

Additional Info

DOE Project Name: Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning
DOE Project Number: EE0008760
DOE Project Lead: Mike Weathers
DOI: 10.15121/1797282
Last Updated: 12 months ago
Apr
2021
Data from April, 2021
Submitted Apr 28, 2021

Contact

Colorado School of Mines


303.273.3768

Status

Publicly accessible License 

Authors

Jim Moraga
Colorado School of Mines
Mahmut Cavur
Kadir Has Universitesi
Hilal Soydan
Colorado School of Mines
H. Sebnem Duzgun
Colorado School of Mines
Ge Jin
Colorado School of Mines

Keywords

geothermal, energy, geodatabase, Nevada, Desert Peak, artificial intelligence, AI, raw data, processed data, remote sensing, hyperspectral, machine learning, deep learning, exploration, ArcGIS, model, site detection, anomaly detection, geothermal site detection, database, hydrothermal, geophysics, radar, short wavelength infrared, SWIR, Support Vector Machine, SVM, land surface temperature, LST, well, GIS, blind, blind system, hyperspectral imaging, geophysical, deformation, conceptual model, fault, preprocessed, geospatial data

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