강연 / 세미나
MINDS Seminar on Data ScienceㅣEarly Tsunami Warning using Convolutional Neural Networks
| 분야Field | |||
|---|---|---|---|
| 날짜Date | 2022-11-08 ~ 2022-11-08 | 시간Time | 10:00 ~ 11:00 |
| 장소Place | Online streaming (Zoom) | 초청자Host | |
| 연사Speaker | Donsub Rim | 소속Affiliation | Washington University in St. Louis |
| TOPIC | MINDS Seminar on Data ScienceㅣEarly Tsunami Warning using Convolutional Neural Networks | ||
| 소개 및 안내사항Content | 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.
https://us06web.zoom.us/j/6888961076?pwd=ejYxN05jNmhUa25PU2JzSUJvQ1haQT09 ID : 688 896 1076 / PW : 54321 |
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| 학회명Field | MINDS Seminar on Data ScienceㅣEarly Tsunami Warning using Convolutional Neural Networks | ||
|---|---|---|---|
| 날짜Date | 2022-11-08 ~ 2022-11-08 | 시간Time | 10:00 ~ 11:00 |
| 장소Place | Online streaming (Zoom) | 초청자Host | |
| 소개 및 안내사항Content | 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.
https://us06web.zoom.us/j/6888961076?pwd=ejYxN05jNmhUa25PU2JzSUJvQ1haQT09 ID : 688 896 1076 / PW : 54321 |
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| 성명Field | MINDS Seminar on Data ScienceㅣEarly Tsunami Warning using Convolutional Neural Networks | ||
|---|---|---|---|
| 날짜Date | 2022-11-08 ~ 2022-11-08 | 시간Time | 10:00 ~ 11:00 |
| 소속Affiliation | Washington University in St. Louis | 초청자Host | |
| 소개 및 안내사항Content | 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.
https://us06web.zoom.us/j/6888961076?pwd=ejYxN05jNmhUa25PU2JzSUJvQ1haQT09 ID : 688 896 1076 / PW : 54321 |
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| 성명Field | MINDS Seminar on Data ScienceㅣEarly Tsunami Warning using Convolutional Neural Networks | ||
|---|---|---|---|
| 날짜Date | 2022-11-08 ~ 2022-11-08 | 시간Time | 10:00 ~ 11:00 |
| 호실Host | 인원수Affiliation | Donsub Rim | |
| 사용목적Affiliation | 신청방식Host | Washington University in St. Louis | |
| 소개 및 안내사항Content | 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.
https://us06web.zoom.us/j/6888961076?pwd=ejYxN05jNmhUa25PU2JzSUJvQ1haQT09 ID : 688 896 1076 / PW : 54321 |
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Admin
·
2022-11-06 18:43 ·
조회 125 
