Geothermal resource favorability: select features and predictions for the United States Great Basin curated for DOI 10.1016/j.geothermics.2025.103450

These datasets were compiled to support the publication Mordensky, S.P., Burns, E.R., Lipor, J.J., DeAngelo, J., 2025. Favorability Mapping for Hydrothermal Power Resource Assessments the Great Basin, USA Geothermics 133, 103450 that discusses how machine learning can predict which locations in a given region are more favorable for geothermal activity. The datasets contained herein are the point data for the 16 input features organized by labeled (i.e., well data in which modeled departures from conductive heat flow serve as labels in supervised machine learning) and unlabeled examples (i.e., grid data without modeled departures from conductive heat flow) used to produce the hydrothermal favorability model for the region delineated as the INnovative Geothermal Exploration through Novel Investigations Of Undiscovered Systems project (INGENIOUS), and hydrothermal favorability maps as prediction percentiles (P05, P25, P50, P75, P95) of the 1,200 realizations from the model presented in Mordensky et al. (2025). Each favorability map depicts an estimate of relative favorability with respect to the other locations (i.e., cells). Corresponding geoTiffs files are located in the subdirectory. Note that the gridFeatureData.csv file has too many examples to open in MS Excel. We suggest opening this file using Notepad or Notepad++. As of ArcGIS Pro 3.3.2, ArcGIS Pro could import and display the data in gridFeatureData.csv file on a computer with at least 64 GB of RAM.