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Training dataset and results for geothermal exploration artificial intelligence, applied to Brady Hot Springs and Desert Peak
The submission includes the labeled datasets, as ESRI Grid files (.gri, .grd) used for training and classification results for our machine leaning model: - brady_som_output.gri, brady_som_output.grd, brady_som_output.* - desert_som_output.gri, desert_som_output.grd, desert_som_output.* The data corresponds to two sites: Brady Hot Springs and Desert Peak, both located near Fallon, NV. Input layers include: - Geothermal: Labeled data (0: Non-geothermal; 1: Geothermal) - Minerals: Hydrothermal mineral alterations, as a result of spectral analysis using Chalcedony, Kaolinite, Gypsum, Hematite and Epsomite - Temperature: Land surface temperature (% of times a pixel was classified as "Hot" by K-Means) - Faults: Fault density with a 300mradius - Subsidence: PSInSAR results showing subsidence displacement of more than 5mm - Uplift: PSInSAR results showing subsidence displacement of more than 5mm Also, the results of the classification using Brady and Desert Peak to build 2 Convolutional Neural Networks. These were applied to the training site as well as the other site, the results are in GeoTiff format. - brady_classification: Results of classification of the Brady-trained model - desert_classification: Results of classification of the Desert Peak-trained model - b2d_classification: Results of classification of Desert Peak using the Brady-trained model - d2b_classification: Results of classification of Brady using the Desert Peak-trained model
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Brady Geodatabase for Geothermal Exploration Artificial Intelligence
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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.
Desert Peak Geodatabase for Geothermal Exploration Artificial Intelligence
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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.
Appendices for Geothermal Exploration Artificial Intelligence Report
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The Geothermal Exploration Artificial Intelligence looks to use machine learning to spot geothermal identifiers from land maps. This is done to remotely detect geothermal sites for the purpose of energy uses. Such uses include enhanced geothermal system (EGS) applications, especially regarding finding locations for viable EGS sites. This submission includes the appendices and reports formerly attached to the Geothermal Exploration Artificial Intelligence Quarterly and Final Reports. The appendices below include methodologies, results, and some data regarding what was used to train the Geothermal Exploration AI. The methodology reports explain how specific anomaly detection modes were selected for use with the Geo Exploration AI. This also includes how the detection mode is useful for finding geothermal sites. Some methodology reports also include small amounts of code. Results from these reports explain the accuracy of methods used for the selected sites (Brady Desert Peak and Salton Sea). Data from these detection modes can be found in some of the reports, such as the Mineral Markers Maps, but most of the raw data is included the DOE Database which includes Brady, Desert Peak, and Salton Sea Geothermal Sites.
Geothermal resource favorability: select features and predictions for the western United States curated for DOI 10.1016/j.geothermics.2023.102662
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The data contained herein are five input features (i.e., heat flow, distance to the nearest quaternary fault, distance to the nearest quaternary magma body, seismic event density, maximum horizontal stress) and labels (i.e., where known geothermal systems have been identified) from Williams and DeAngelo (2008) and nine favorability maps from Mordensky et al. (2023). The favorability maps are the untransformed predictions from models resulting from the features and labels used with either the methods presented in Williams and DeAngelo (2008) or the machine learning approaches presented in Mordensky et al. (2023). Each favorability map depicts an estimate of relative favorability with respect to the other locations (i.e., cells), allowing for a comparison of the influence the different methods and machine learning approaches produced when predicting geothermal favorability. The machine learning approaches sought to minimize the influence of expert bias imparted by the methods from Williams and DeAngelo (2008). The favorability maps presented from the models that used the methods from Williams and DeAngelo (2008) are provided for comparative purposes.
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