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Brady's Geothermal Field DAS Vibroseis Data

The submitted data correspond to the monitored vibrations caused by a vibroseis seismically exciting the ground in the vertical direction and captured by the DAS horizontal and vertical arrays during the PoroTomo Experiment. The data also include a file with the acceleration recor...

Feigl, K. University of Wisconsin

Mar 25, 2016
11 Resources
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Publicly accessible

EGS Collab Experiment 2: 4100 Foot Level Elastic Property Data

Static and dynamic elastic properties (Young's modulus, Shear Modulus, P-wave Modulus, and Poisson's Ratio) of amphibolites and rhyolites from the TV4100 and TH4100 boreholes at the Sanford Underground Research Laboratory (SURF). Elastic properties include Young's modulus, Poisson...

Jahnke, B. et al Lawrence Berkeley National Laboratory

Dec 18, 2020
4 Resources
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Utah FORGE: 2023 Induced Seismicity Mitigation Plan

This is the updated Utah FORGE Phase 3B induced seismicity mitigation plan (ISMP) covering the general Utah FORGE area. This new version incorporates newly collected seismic data, including data collected during the 2022 stimulation and a probabilistic seismic hazard assessment (...

Pankow, K. et al University of Utah Seismograph Stations

Aug 09, 2023
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Utah FORGE: Induced Seismicity Mitigation Plan

This is the current induced seismicity mitigation plan (ISMP) for the Utah FORGE project. Information that was collected during Phases 1 and 2 of the Utah FORGE project has been incorporated, as have literature searches and risk assessments. The purpose of this report is to identi...

Utah, U. et al Energy and Geoscience Institute at the University of Utah

Dec 30, 2020
2 Resources
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Curated

Brady's Geothermal Field DAS Earthquake Data

The submitted data correspond to the vibration caused by a 3.4 M earthquake and captured by the DAS horizontal and vertical arrays during the PoroTomo Experiment. Data is available in the original .sgy formats, as well as in standardized .h5 formats. Earthquake information : M ...

Feigl, K. University of Wisconsin

Mar 21, 2016
10 Resources
0 Stars
Publicly accessible

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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