首页|Study Findings from Barcelona Supercomputing Center Advance Knowledge in Machine Learning (A machine learning estimator trained on synthetic data for real-time earthquake ground-shaking predictions in Southern California)
Study Findings from Barcelona Supercomputing Center Advance Knowledge in Machine Learning (A machine learning estimator trained on synthetic data for real-time earthquake ground-shaking predictions in Southern California)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in artific ial intelligence. According to news reporting out of the Barcelona Supercomputin g Center by NewsRx editors, research stated, “After large-magnitude earthquakes, a crucial task for impact assessment is to rapidly and accurately estimate the ground shaking in the affected region.” Our news editors obtained a quote from the research from Barcelona Supercomputin g Center: “To satisfy real-time constraints, intensity measures are traditionall y evaluated with empirical Ground Motion Models that can drastically limit the a ccuracy of the estimated values. As an alternative, here we present Machine Lear ning strategies trained on physics-based simulations that require similar evalua tion times. We trained and validated the proposed Machine Learning-based Estimat or for ground shaking maps with one of the largest existing datasets (<100M simulated seismograms) from CyberShake developed by the Southern California Earthquake Center covering the Los Angeles basin. For a well-tailored synthetic database, our predictions outperform empirical Ground Motion Models provided th at the events considered are compatible with the training data.” According to the news editors, the research concluded: “Using the proposed strat egy we show significant error reductions not only for synthetic, but also for fi ve real historical earthquakes, relative to empirical Ground Motion Models.”
Barcelona Supercomputing CenterCyborgsEmerging TechnologiesMachine Learning
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