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Established in 2020, POSTECH Mathematical Institute for Data Science (MINDS) is the community of researchers in the areas of fundamental data science, machine learning, artificial intelligence, scientific computing, and humanitarian data science. MINDS mission is to provide a platform for collaboration among researchers and to provide various opportunities for students in data science. MINDS also aims to use our data science research to serve our local and global communities pursuing humanitarian data science.

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MINDS Seminar Series | Donsub Rim (Washington University in St. Louis) - Early Tsunami Warning using Convolutional Neural Networks

period : 2022-11-08 ~ 2022-11-08
time : 10:00:00 ~ 11:00:00
개최 장소 : Online streaming (Zoom)
Topic : Early Tsunami Warning using Convolutional Neural Networks
개요
Date 2022-11-08 ~ 2022-11-08 Time 10:00:00 ~ 11:00:00
Speaker Donsub Rim Affiliation Washington University in St. Louis
Place Online streaming (Zoom) Streaming link ID : 688 896 1076 / PW : 54321
Topic Early Tsunami Warning using Convolutional Neural Networks
Contents In a tsunamigenic earthquake event, predicting tsunami waveheights early in the event can aid emergency management. However, such prediction is a challenging problem: The tsunami wave propagates according to nonlinear partial differential equations with unknown initial conditions. In this talk, we will discuss a data-driven approach: we simulate random earthquakes and resulting tsunamis to form a training dataset, then train convolutional neural networks to make predictions. We show two recent results that focus on events originating from the Cascadia Subduction Zone and reach the Strait of Juan de Fuca. We first show that 30-60 minutes of waveheight measurements at the entrance of the Strait is sufficient to predict 6 hours of tsunami waveform in the interior of the strait. Next, we show that 4 minutes of seismic measurements from Global Navigation Satellite System (GNSS) is sufficient to make predictions of comparable accuracy.
MinDS MinDS · 2022-10-04 10:22 · Views 433

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