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3D Model of the McGinness Hills Geothermal Area

The McGinness Hills geothermal system lies in a ~8.5 km wide, north-northeast trending accommodation zone defined by east-dipping normal faults bounding the Toiyabe Range to the west and west-dipping normal faults bounding the Simpson Park Mountains to the east. Within this broad ...

E. Faulds, J. University of Nevada

Dec 31, 2013
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GEOPHIRES Simulations for Deep Direct Use (DDU) Projects

This folder contains the GEOPHIRES codes and input files for running the base case scenarios for the six deep direct-use (DDU) projects. The six DDU projects took place during 2017-2020 and were funded by the U.S. Department of Energy Geothermal Technologies Office. They investiga...

Beckers, K. National Renewable Energy Laboratory

Jun 30, 2020
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Brady Geodatabase for Geothermal Exploration Artificial Intelligence

These files contain the geodatabases related to Brady's Geothermal Field. It includes all input and output files for the Geothermal Exploration Artificial Intelligence. Input and output files are sorted into three categories: raw data, pre-processed data, and analysis (post-proces...

Moraga, J. et al Colorado School of Mines

Apr 27, 2021
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Desert Peak Geodatabase for Geothermal Exploration Artificial Intelligence

These files contain the geodatabases related to the Desert Peak Geothermal Field. It includes all input and output files used in the project. The files include data categories of raw data, pre-processed data, and analysis (post-processed data). In each of these categories there ar...

Moraga, J. et al Colorado School of Mines

Apr 27, 2021
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Graph Theory for Analyzing Pair-wise Data: Application to Interferometric Synthetic Aperture Radar Data

Graph theory is useful for estimating time-dependent model parameters via weighted least-squares using interferometric synthetic aperture radar (InSAR) data. Plotting acquisition dates (epochs) as vertices and pair-wise interferometric combinations as edges defines an incidence gr...

Reinisch, E. University of Wisconsin

Jul 28, 2016
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Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs

Subsurface data analysis, reservoir modeling, and machine learning (ML) techniques have been applied to the Brady Hot Springs (BHS) geothermal field in Nevada, USA to further characterize the subsurface and assist with optimizing reservoir management. Hundreds of reservoir simulat...

Beckers, K. et al National Renewable Energy Laboratory

Feb 18, 2021
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3D Model of the Tuscarora Geothermal Area

The Tuscarora geothermal system sits within a ~15 km wide left-step in a major west-dipping range-bounding normal fault system. The step over is defined by the Independence Mountains fault zone and the Bull Runs Mountains fault zone which overlap along strike. Strain is transferre...

E., J. University of Nevada

Dec 31, 2013
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Deep Sedimentary Basin EGS Development

Stratigraphic reservoirs with high permeability and temperature at economically accessible depths are attractive for power generation because of their large areal extent (> 100 km2) compared to the fault controlled hydrothermal reservoirs (< 10 km2) found throughout much of the we...

Allis, R. and Moore, J. University of Utah

Jan 24, 2013
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Appendices for Geothermal Exploration Artificial Intelligence Report

The Geothermal Exploration Artificial Intelligence looks to use machine learning to spot geothermal identifiers from land maps. This is done to remotely detect geothermal sites for the purpose of energy uses. Such uses include enhanced geothermal system (EGS) applications, especia...

Duzgun, H. et al Colorado School of Mines

Jan 08, 2021
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Brady Hot Springs Seismic Modeling Data for Push-Pull Project

This submission includes synthetic seismic modeling data for the Push-Pull project at Brady Hot Springs, NV. The synthetic seismic is all generated by finite-difference method regarding different fracture and rock properties.

Zhang, R. University of Louisiana

Jul 31, 2018
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