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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
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Data for Uncontained Ruptures Reduce Energetics of Triggered Seismicity: Laboratory Fault Reactivation Experiments

The dataset contains raw mechanical data from laboratory fault reactivation experiments on pre-stressed granite/granitoid samples, along with experimental parameters and MATLAB processing code for data deduction. The raw time-series data were recorded using a triaxial-shear appara...

Elsworth, D. et al Pennsylvania State University

Apr 13, 2026
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Processed Lab Data for Neural Network-Based Shear Stress Level Prediction

Data for Uncontained Ruptures Reduce Energetics of Triggered Seismicity: Laboratory Fault Reactivation Experiments