Geothermal resource favorability in the Great Basin: select features and predictions curated for DOI 10.3133/sir20265071

These datasets were compiled to support the publication Mordensky and others (2026) that evaluates the number of realizations (train-test splits) necessary to ensure robust estimates of conventional hydrothermal resource favorability when using Monte Carlo cross-validation with supervised machine learning. The datasets contained herein are the point data for the 53 publicly available 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; Ayling and others [2022]), and the median hydrothermal favorability map (prediction percentile = P50) of the 1800 realizations from the model presented in Mordensky and others (2026). 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 grid_features.csv and median_predicions_hydrothermal_favorability.csv files have too many examples to open in Microsoft Excel. We suggest opening this file using Notepad or Notepad++. As of ArcGIS Pro 3.5.5, ArcGIS Pro could import and display the data in gridFeatureData.csv file on a computer with at least 64 GB of RAM.