Salton Sea Geodatabase for Geothermal Exploration Artificial Intelligence

Moraga, Jim; Cavur, Mahmut; Soydan, Hilal; Duzgun, H. Sebnem; Jin, Ge

doi:10.15121/1797283

Title: Salton Sea Geodatabase for Geothermal Exploration Artificial Intelligence

Dataset
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Abstract

These files contain the geodatabases related to Salton Sea Geothermal Field. It includes all input and output files used with 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. The files are used with the Geothermal Exploration Artificial Intelligence for the Salton Sea Geothermal Site 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 Salton Sea Geothermal Site.

Authors:

;

Publication Date:: 2021-04-27

Other Number(s):: 1306

DOE Contract Number:: EE0008760

Research Org.:: USDOE Geothermal Data Repository (United States); Colorado School of Mines, Golden, CO (United States)

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Program (EE-4G)

Collaborations:: Colorado School of Mines

Subject:: 15 Geothermal Energy

Keywords:: geothermal; energy; geodatabase; Salton Sea; artificial intelligence; ai; deep learning; machine learning; seismic; remote sensing; hyperspectral; hyperspectral imaging; geospacial database; 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; California; ArcGis; GIS; model; database; hydrothermal; geophysics; radar; blind; blind system; deformation; geophysical; conceptual model fault; preprocessed; raster; vector; field data; geospatial data

Geolocation:: 33.4,-115.4|33.0,-115.4|33.0,-115.8|33.4,-115.8|33.4,-115.4

OSTI Identifier:: 1797283

DOI:: https://doi.org/10.15121/1797283

Project Location:

Citation Formats


                    Moraga, Jim, Cavur, Mahmut, Soydan, Hilal, Duzgun, H. Sebnem, and Jin, Ge. Salton Sea Geodatabase for Geothermal Exploration Artificial Intelligence.  United States: N. p., 2021. 
        Web.  doi:10.15121/1797283.

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                    Moraga, Jim, Cavur, Mahmut, Soydan, Hilal, Duzgun, H. Sebnem, & Jin, Ge. Salton Sea Geodatabase for Geothermal Exploration Artificial Intelligence.  United States.  doi:https://doi.org/10.15121/1797283

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                    Moraga, Jim, Cavur, Mahmut, Soydan, Hilal, Duzgun, H. Sebnem, and Jin, Ge. 2021.  
"Salton Sea Geodatabase for Geothermal Exploration Artificial Intelligence".  United States.  doi:https://doi.org/10.15121/1797283.  https://www.osti.gov/servlets/purl/1797283. Pub date:Tue Apr 27 00:00:00 EDT 2021

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@article{osti_1797283,

  title        = {Salton Sea Geodatabase for Geothermal Exploration Artificial Intelligence},

  author       = {Moraga, Jim and Cavur, Mahmut and Soydan, Hilal and Duzgun, H. Sebnem and Jin, Ge},

  abstractNote = {These files contain the geodatabases related to Salton Sea Geothermal Field. It includes all input and output files used with 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. The files are used with the Geothermal Exploration Artificial Intelligence for the Salton Sea Geothermal Site 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 Salton Sea Geothermal Site.},

  doi          = {10.15121/1797283},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {2021},

  month        = {4}

}

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DOI: https://doi.org/10.15121/1797283

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