首页|Studies from San Diego State University Describe New Findings in Machine Learning (Separating Injection-driven and Earthquakedriven Induced Seismicity By Combi ning a Fully Coupled Poroelastic Model With Interpretable Machine Learning)
Studies from San Diego State University Describe New Findings in Machine Learning (Separating Injection-driven and Earthquakedriven Induced Seismicity By Combi ning a Fully Coupled Poroelastic Model With Interpretable Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting originating in San Diego, Cal ifornia, by NewsRx journalists, research stated, "In areas of induced seismicity,earthquakes can be triggered by stress changes due to fluid injection and stat ic deformation from fault slip. Here we present a method to distinguish between injection-driven and earthquake-driven triggering of induced seismicity by combi ning a calibrated, fully coupled, poroelastic stress model of wastewater injecti on with interpretation of a machine learning algorithm trained on both earthquak e catalog and modeled stress features." Funders for this research include National Science Foundation (NSF), San Diego S tate University, National Science Foundation (NSF).
San DiegoCaliforniaUnited StatesNo rth and Central AmericaCyborgsEmerging TechnologiesMachine LearningSan D iego State University
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