今週のうなぎセミナーについてお知らせいたします。

Here is information of the Unagi-seminar(Apr 22).

************** Seminar on Seismology IV A,C /地震学ゼミナールIVＡ,C (Unagi Seminar) **************

科目：地震学ゼミナールIV A, C / Seminar on Seismology IV A, C（修士・博士）
日時 : 2021年 04月 22日 (木) 14:00~
場所 : オンライン(Zoom)

Date and Time: 2021-04-22, 14:00~
Place: Zoom

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Presenter 1 (発表者 1): 森永 優 (Morinaga Masaru)
Title (題目):
自己紹介 (Self-Introduction)
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Presenter 2 (発表者 2): 山本 誠 (Yamamoto Makoto)
Title (題目):
自己紹介 (Self-Introduction)
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Presenter 3 (発表者 3): 村本 智也 (Muramoto Tomoya)
Title (題目):
自己紹介 (Self-Introduction)
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Presenter 4 (発表者 4): 加藤　慎也 (Katoh Shinya)
Title (題目):
The automatic arrival-time picking and determination of P-wave first-motion polarity using Deep Learning.
Abstract (要旨):
　Accurate seismic arrival-times and P-wave first-motion polarities are required to detect accurate earthquake location, to estimate a velocity structure, and to determine focal mechanisms. In recent years, the number of observation data has been increasing. In particular, we have installed high dense seismic station networks (Manten network) with 250 Hz sampling in Kinki, San-in and Nagano (Miura et al., 2010; Iio., 2010; Iio et al., 2017). The amount of data used for analysis has been increasing as seismic stations have been increasing, but a workload has also been increasing, since arrival-time picking and polarity determining is a very time-consuming task.
　In this study, in order to reduce the workload, we made an automatic arrival-time picking model and an automatic determination of P-wave first-motion polarity model. An algorithm used to make the model is PhaseNet (Zhu and Beroza., 2018) and the Convolutional Neural Network(CNN).
　In this presentation, the accuracy evaluation of those models and the results by those models will be presented.

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