Geothermal resource favorability: select features and predictions for the western United States curated for DOI 10.1016/j.geothermics.2023.102662

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...

Tags: ANN, Arizona, California, Colorado, Energy Resources, All tags... Idaho, Montana, Nevada, New Mexico, Oregon, SVM, USGS Science Data Catalog (SDC), Utah, Washington, Western United States, Wyoming, XGBoost, artificial neural network, data driven, data-driven, eXtreme Gradient Boosting, feed forward neural network, geothermal, geothermal favorability, geothermal resource assessment, heat flow, logistic regression, machine learning, magmatism, multilayer perceptron neural network, quaternary faults, seismicity, stress, support-vector machine, weight of evidence, weight-of-evidence, Fewer tags