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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... -
Utah FORGE: Fluid Injection-Rate Controls on Seismic Moment from Laboratory Fault Reactivation Experiments
This dataset contains experimental and acoustic data from shear reactivation tests that investigate the relationship between fluid-injection rate, pore pressure distribution,... -
Utah FORGE: Laboratory Shear Experiments Linking Fault Roughness, Friction, Permeability, and P-Wave Characteristics
This dataset contains results from five laboratory shear experiments on gneiss and granitoid samples from the Utah FORGE site, conducted at Penn State University. The... -
Utah FORGE: Friction-Permeability-Seismicity Laboratory Experiments with Non-Linear Acoustics
Laboratory experimental data on saw-cut interface of Westerly Granite and Utah Forge granitoid rocks. Experiments include velocity-stepping and fluid pressure stepping... -
Utah FORGE: Slide-Hold-Slide Experiments on Gneiss at Increased Temperature
Included are data from triaxial, single-inclined-fracture friction experiments. The experiments were performed with slide-hold-slide protocol on Utah FORGE gneiss at increased...
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