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Processed Lab Data for Neural Network-Based Shear Stress Level Prediction

Machine learning can be used to predict fault properties such as shear stress, friction, and time to failure using continuous records of fault zone acoustic emissions. The files are extracted features and labels from lab data (experiment p4679). The features are extracted with a n...

Marone, C. et al Pennsylvania State University

May 14, 2021
3 Resources
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Fallon FORGE: Gravity and Magnetics Data

This package contains principal facts for new gravity data collected September November 2017 in support of the Fallon FORGE project. Also included are rock core density and magnetic susceptibility data for key core intervals, used in modeling 2D and 3D gravity inversions. Indivi...

Blankenship, D. et al Sandia National Laboratories

Mar 09, 2018
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Hawthorne Nevada Deep Direct-Use Feasibility Study 3D Geologic Model

The objective of this project is to use a multi-disciplinary, three-tiered approach to assess the geothermal resource and determine the feasibility of implementing a large-scale, direct-use facility for the Hawthorne Army Depot (HAD) and the various town and county facilities in H...

Lowry, T. Nevada Bureau of Mines and Geology

Mar 29, 2018
3 Resources
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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
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GIS Resource Compilation Map Package Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

This submission contains an ESRI map package (.mpk) with an embedded geodatabase for GIS resources used or derived in the Nevada Machine Learning project, meant to accompany the final report. The package includes layer descriptions, layer grouping, and symbology. Layer groups incl...

Brown, S. et al Nevada Bureau of Mines and Geology

Jun 01, 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
3 Resources
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Brady's Geothermal Field Nodal Seismometer Data

This submission includes links to raw data, field notes, metadata, and p-wave arrival auto-picks from processed data (not provided) from the nodal seismometer array deployed at the PoroTomo Natural Laboratory in Brady's Hot Springs, Nevada during the March 2016 testing. The data i...

Parker, L. University of Wisconsin

Mar 30, 2016
7 Resources
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Brady's Geothermal Field March 2016 Vibroseis SEG-Y Files and UTM Locations

PoroTomo March 2016 Updated vibroseis source locations with UTM locations. Supersedes gdr.openei.org/submissions/824. Updated vibroseis source location data for Stages 1-4, PoroTomo March 2016. This revision includes source point locations in UTM format (meters) for all four Stage...

Feigl, K. University of Wisconsin

Mar 31, 2016
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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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