首页|Findings from University of Utrecht Update Knowledge of Machine Learning (Strain Localization In Sandstone-derived Fault Gouges Under Conditions Relevant To Ear thquake Nucleation)
Findings from University of Utrecht Update Knowledge of Machine Learning (Strain Localization In Sandstone-derived Fault Gouges Under Conditions Relevant To Ear thquake Nucleation)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News - Researchers detail new data in Machine Learning. According to news reporting originating from Utrecht, Netherlands, by NewsRx correspondents, research stated, “Constraining strain localization and the growth of shear fabrics within brittle fault zones at sub-seismic slip rates is important for understanding fault strength and frictional stability. We condu cted direct shear experiments on simulated sandstone-derived fault gouges at an effective normal stress of 40 MPa, a pore pressure of 15 MPa, and a temperature of 100 degrees C. Using a passive strain marker and X-ray Computed Tomography, w e analyzed the spatial distribution of deformation in gouges deformed in the str ain-hardening, subsequent strain-softening, and then steady-state regimes at dis placement rates of 1, 30, and 1,000 mu m/s.”
UtrechtNetherlandsEuropeCyborgsE merging TechnologiesMachine LearningUniversity of Utrecht
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