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Coso Geothermal Spectral Library for Rocks and Minerals

Aug 23, 2023
380.31 MB
Publicly accessible
An integrated open mineral spectral library designed to enhance the utility and precision of mineral spectral data for geothermal exploration, developed from a reliable and comprehensive digital dataset for seamless sharing by integrating field data, the USGS spectral library, and...
Authors
Cavur, M. et al Mining Engineering Department of Colorado School of Mines
geothermalenergyhydrothermalspectroscopyspectral databasecosogeologycoso geothermal fieldgcfcaliforniaspectral libraryhydrothermal alterationalteration mineralsspectral dataelemental enrichmentscanning electron microscopyx-ray fluorescencespectrometry

Geothermal Play Fairway Analysis for Low-Temperature Resources in the Denver Basin

Sep 30, 2023
404.21 MB
Publicly accessible
This dataset is part of an effort to highlight the advantages of incorporating low-temperature (< 150 C) geothermal resource evaluation into the implementation of combined heat and power (CHP), and geothermal direct use (GDU) technologies (e.g., space heating and/or cooling). For...
Authors
Davalos-Elizondo, E. et al National Renewable Energy Laboratory
geothermalenergypfaplay fairway analysisdenver basinsedimentary basincoloradogeospatialfavorabilitythermalrisk dataexplorationgeologygeologic dataeconomic datawater co-productiongroundwater levelshot spring locationsgeochemistryfaultsearthquakeseconomic feasibilitypythonresource favoribility mapsgeoreportgeopfaworkflowlow-temperature

Machine Learning to Identify Geologic Factors Associated with Production in Geothermal Fields: A Case-Study Using 3D Geologic Data from Brady Geothermal Field and NMFk

Oct 01, 2021
5.68 MB
Publicly accessible
In this paper, we present an analysis using unsupervised machine learning (ML) to identify the key geologic factors that contribute to the geothermal production in Brady geothermal field. Brady is a hydrothermal system in northwestern Nevada that supports both electricity producti...
Authors
Siler, D. et al United States Geological Survey
geothermalenergynmfkbrady hot springsmachine learningmlbhsnonnegative matrix factorization k-meanshydrothermalbradyk-meansclusteringnonnegative matrix factorizationmatrix factorizationgeothermalcloudsmarttensorsunsupervised3d well data3d geologic mapgeologic structurefaultsstressgeologycharacterizationgeologic modelproductioncode
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  • The GDR provides free access to data generated from projects funded by the U.S. Department of Energy's Office of Geothermal.
  • Content is available under Creative Commons Attribution 4.0 unless otherwise noted.

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