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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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3 Resources
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kISMET Final Report and Highlights
The files in this submission describes the results of a series of stress measurement and hydraulic fracturing experiments conducted at the Sanford Underground Research Facility (SURF) in Lead, SD. This report describes the accomplishments of the kISMET (permeability (k) and Induce...
Oldenburg, C. et al Lawrence Berkeley National Laboratory
Oct 31, 2016
2 Resources
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2 Resources
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Seismic Analysis of Spatio-Temporal Fracture Generation During EGS Resource Development Deviatoric MT, Fracture Network, and Final Report
This submission contains 167 deviatoric moment tensor (MT) solutions for the seismicity observed two years prior and three years post start of injection activities at The Geysers Prati 32 EGS Demonstration. Also included is a statistical representation of the properties of 751 fra...
Gritto, R. et al Array Information Technology
Sep 01, 2018
3 Resources
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3 Resources
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Hybrid machine learning model to predict 3D in-situ permeability evolution
Enhanced geothermal systems (EGS) can provide a sustainable and renewable solution to the new energy transition. Its potential relies on the ability to create a reservoir and to accurately evaluate its evolving hydraulic properties to predict fluid flow and estimate ultimate therm...
Elsworth, D. and Marone, C. Pennsylvania State University
Nov 22, 2022
4 Resources
0 Stars
Publicly accessible
4 Resources
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Publicly accessible