Publications and Datasets from Play-Fairway Retrospective Analysis with Emphasis on Developing Improved Hydrothermal Energy Assessments

Previous moderate- and high-temperature geothermal resource assessments of the western United States utilized data-driven methods and expert decisions to estimate resource favorability. Although expert decisions can add confidence to the modeling process by ensuring reasonable models are employed, expert decisions also introduce human and, thereby, model bias. This bias can present a source of error that reduces the predictive performance of the models and confidence in the resulting resource estimates.

This study aims to develop robust data-driven methods with the goals of reducing bias and improving predictive ability. This submission includes a list of papers, data releases, and presentations produced as part of this work.

Data and Resources

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identifier https://data.openei.org/submissions/7589
issued 2023-02-07T07:00:00Z
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license https://creativecommons.org/licenses/by/4.0/
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programCode {019:006}
projectLead Mike Weathers
projectNumber 24996
projectTitle Play-Fairway Retrospective Analysis with Emphasis on Developing Improved Hydrothermal Energy Assessments
publisher United States Geological Survey
resource-type Dataset
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Groups
  • AmeriGEOSS
  • National Provider
  • North America
Tags
  • AmeriGEO
  • AmeriGEOSS
  • CKAN
  • GEO
  • GEOSS
  • National
  • North America
  • United States
  • bias-reduction
  • characterization
  • data-driven
  • egs
  • energy
  • energy-assessment
  • favorability
  • geoscience
  • geothermal
  • hydrothermal
  • low-temp
  • machine-learning
  • mapping
  • pfa
  • processed-data
  • resource
  • resource-assessment
  • retrospective
  • western-us
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maintainer Stanley P. Mordensky
maintainer_email smordensky@usgs.gov
metadata_created 2025-09-23T19:24:42.617128
metadata_modified 2025-09-23T19:24:42.617135
notes Previous moderate- and high-temperature geothermal resource assessments of the western United States utilized data-driven methods and expert decisions to estimate resource favorability. Although expert decisions can add confidence to the modeling process by ensuring reasonable models are employed, expert decisions also introduce human and, thereby, model bias. This bias can present a source of error that reduces the predictive performance of the models and confidence in the resulting resource estimates. This study aims to develop robust data-driven methods with the goals of reducing bias and improving predictive ability. This submission includes a list of papers, data releases, and presentations produced as part of this work.
num_resources 7
num_tags 27
title Publications and Datasets from Play-Fairway Retrospective Analysis with Emphasis on Developing Improved Hydrothermal Energy Assessments