Sponsor
This work was supported by the U.S. Geological Survey Energy Resources Program, the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE), Geothermal Technologies Office (GTO) under Contract No. DEAC02-05CH11231 with Lawrence Berkeley National Laboratory, Conformed Federal Order No. 7520443 between Lawrence Berkeley National Laboratory and the U.S. Geological Survey (Award Number DE-EE0008105), and Standard Research Subcontract No. 7572843 between Lawrence Berkeley National Laboratory and Portland State University. Additional support for John Lipor was provided by the National Science Foundation award NSF CAREER CIF-2046175. We utilized the data made available through the INnovative Geothermal Exploration through Novel Investigations Of Undiscovered Systems project (INGENIOUS), which is supported by the U.S. Department of Energy - Geothermal Technologies Office under award DE-EE0009254 to the University of Nevada, Reno.
Published In
Geothermics
Document Type
Article
Publication Date
12-1-2025
Subjects
Geothermal resource assessment, Hydrothermal favorability, Great Basin, Data-driven -- Machine learning
Abstract
Highlights
- • The new approach to predict hydrothermal resource favorability for the U.S. Great Basin is a synthesis of modern data-driven machine learning improvements from the last several years and predicts 85 % of power-producing systems with operating power plants in the most favorable 10 % of the total area with over half of the power-producing systems (10 of the 19 exposed power-producing systems and 5 of the 9 hidden power-producing systems) in the 99th percentile.
- • The new hydrothermal favorability map predicts both hidden and exposed power-producing hydrothermal systems equally well.
- • The new hydrothermal favorability map preferentially predicts systems with greater convective heat flow than systems with comparatively less convective heat flow.
- • The new hydrothermal favorability map outperforms previous mapping approaches due to improvements in methods, improved input data/evidence layers, and a significant increase in the number of training sites.
- • Monte Carlo cross-validation (n = 1200) is used to estimate predictive uncertainty in the favorability maps, with the final best map being the median prediction for each location.
Rights
Copyright (c) 2025 The Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
Locate the Document
DOI
10.1016/j.geothermics.2025.103450
Persistent Identifier
https://archives.pdx.edu/ds/psu/44103
Publisher
Elsevier BV
Citation Details
Mordensky, S. P., Burns, E. R., Lipor, J. J., & DeAngelo, J. (2025). Favorability mapping for hydrothermal power resource assessments of the Great Basin, USA. Geothermics, 133, 103450.