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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
12 Resources
0 Stars
Publicly accessible
12 Resources
0 Stars
Publicly accessible
DASH Slow Strain Rates from Brady Hot Springs Geothermal Field during PoroTomo Deployment Period
This submission contains slow strain rates summed to radians over 30 second intervals [rad/s] derived from horizontal distributed acoustic sensing measurements (DASH) of Brady geothermal field during PoroTomo deployment (2016-Mar-14 to 2016-Mar-26). There is one file correspondin...
Reinisch, E. et al University of Wisconsin
Jun 27, 2018
20 Resources
0 Stars
Curated
20 Resources
0 Stars
Curated
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
3 Resources
0 Stars
Publicly accessible
3 Resources
0 Stars
Publicly accessible
Training dataset and results for geothermal exploration artificial intelligence, applied to Brady Hot Springs and Desert Peak
The submission includes the labeled datasets, as ESRI Grid files (.gri, .grd) used for training and classification results for our machine leaning model:
brady_som_output.gri, brady_som_output.grd, brady_som_output.*
desert_som_output.gri, desert_som_output.grd, desert_som_outpu...
Moraga, J. et al Colorado School of Mines
Sep 01, 2020
16 Resources
0 Stars
Publicly accessible
16 Resources
0 Stars
Publicly accessible
Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results
Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells increasing or decreasing the fluid flow rates across the wells and drilling new wells at appropriate locations. Th...
Beckers, K. et al National Renewable Energy Laboratory
Oct 20, 2021
6 Resources
0 Stars
Publicly accessible
6 Resources
0 Stars
Publicly accessible
3-D Geologic Controls of Hydrothermal Fluid Flow at Brady Geothermal Field, Nevada using PCA
In many hydrothermal systems, fracture permeability along faults provides pathways for groundwater to transport heat from depth. Faulting generates a range of deformation styles that cross-cut heterogeneous geology, resulting in complex patterns of permeability, porosity, and hydr...
Siler, D. and Pepin, J. United States Geological Survey
Oct 01, 2021
4 Resources
0 Stars
Publicly accessible
4 Resources
0 Stars
Publicly accessible
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
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...
Siler, D. et al United States Geological Survey
Oct 01, 2021
6 Resources
0 Stars
Publicly accessible
6 Resources
0 Stars
Publicly accessible
PoroTomo: Horizontal Distributed Acoustic Sensing (DAS) Measurements During an M 2.3 Explosion
Included here are Distributed Acoustic Sensing (DAS) data collected by the horizontal DAS array at Brady's Hot Springs Geothermal Field. The system recorded this data during an M 2.3 explosion at the Nevada Test Site (NTS), which is located approximately 400km southeast of the fie...
Kratt, C. et al Center for Transformative Environmental Monitoring Programs (CTEMPs)
Dec 18, 2018
5 Resources
0 Stars
Curated
5 Resources
0 Stars
Curated
PoroTomo Natural Laboratory Horizontal and Vertical Distributed Acoustic Sensing Data
This dataset includes links to the PoroTomo DAS data in both SEG-Y and hdf5 (via h5py and HSDS with h5pyd) formats with tutorial notebooks for use. Data are hosted on Amazon Web Services (AWS) Simple Storage Service (S3) through the Open Energy Data Initiative (OEDI). Also include...
Feigl, K. et al University of Wisconsin
Mar 29, 2016
20 Resources
1 Stars
Curated
20 Resources
1 Stars
Curated
Maps, Models and Data from Southeastern Great Basin PFA
This submission includes composite risk segment models in raster format for permeability, heat of the earth, and MT, as well as the final PFA model of geothermal exploration risk in Southwestern Utah, USA. Additionally, this submission has data regarding hydrothermally altered are...
Nash, G. Energy and Geoscience Institute at the University of Utah
Jun 30, 2017
7 Resources
0 Stars
Publicly accessible
7 Resources
0 Stars
Publicly accessible
Rare Earth Element and Trace Element Data Associated with Hydrothermal Spring Reservoir Rock, Idaho
These data represent rock samples collected in Idaho that correspond with naturally occurring hydrothermal samples that were collected and analyzed by INL (Idaho Falls, ID). Representative samples of type rocks were selected to best represent the various regions of Idaho in which ...
Quillinan, S. and Bagdonas, D. University of Wyoming
Jun 22, 2017
1 Resources
0 Stars
Publicly accessible
1 Resources
0 Stars
Publicly accessible
Steptoe Valley NV Data Compilation: Understanding a Stratigraphic Hydrothermal Resource through Geophysical Imaging
Sandia National Laboratories partnered with a multi-disciplinary group of subject matter experts to evaluate a stratigraphic geothermal resource in Steptoe Valley, Nevada using both established and novel geophysical imaging techniques. Provided here are a compilation of newly acqu...
Schwering, P. et al Sandia National Laboratories
Nov 01, 2023
8 Resources
0 Stars
Publicly accessible
8 Resources
0 Stars
Publicly accessible
Utah FORGE: Seismic and Other Shapefiles from the Roosevelt Hot Springs Area
Three shapefiles in this submission show the position of proposed seismic line surveys. The mid-crustal velocity anomaly file shows the extent of an anomalously low P-wave velocity zone in the subsurface. Two other files show the extent of known hydrothermal systems in the Rooseve...
Moore, J. Energy and Geoscience Institute at the University of Utah
Mar 22, 2016
7 Resources
0 Stars
Publicly accessible
7 Resources
0 Stars
Publicly accessible
Validation of Innovative Exploration Technologies for Newberry Volcano: Drill Site Location Map
Newberry seeks to explore "blind" (no surface evidence) convective hydrothermal systems associated with a young silicic pluton on the flanks of Newberry Volcano. This project will employ a combination of innovative and conventional techniques to identify the location of subsurface...
Jaffe, T. Davenport Newberry Holdings, LLC
Jan 01, 2012
1 Resources
0 Stars
Publicly accessible
1 Resources
0 Stars
Publicly accessible
Utah FORGE: Mineral Mountains West Fault System Report
This archive contains a report on the Mineral Mountains West fault system as part of phase 2C and the evidence for the northern terminus of this structure. Geologic field mapping, reprocessing of 3D seismic reflection data, and soil gas surveys contributed to this effort.
Simmons, S. and Miller, J. Energy and Geoscience Institute at the University of Utah
Jul 01, 2019
1 Resources
0 Stars
Publicly accessible
1 Resources
0 Stars
Publicly accessible
DEEPEN Leapfrog Geodata Model Cleaned and Reformatted Exploration Datasets from Newberry Volcano
DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments.
As part of the DEEPEN 3D play fairway analysis (PFA) conducted at Newberry Volcano for multiple play types (conventional hydrothermal, superhot EGS, and supercritical), existing geoscientific e...
Pauling, H. et al National Renewable Energy Laboratory
Jun 30, 2023
22 Resources
0 Stars
Publicly accessible
22 Resources
0 Stars
Publicly accessible