Room A

[S24]AM-1

Sat. Oct 31, 2020 9:00 AM - 10:15 AM ROOM A

chairperson:Takahiko Uchide(Geological Survey of Japan, AIST), chairperson:Hisahiko Kubo(NIED), chairperson:Yuki Kodera(Meteorological Research Agency, JMA)

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9:00 AM - 9:15 AM

[S24-01] Regional characteristic of seismic activity inferred from unsupervised machine learning of F-net moment-tensor catalog

〇Hisahiko Kubo¹, Takeshi Kimura¹, Katsuhiko Shiomi¹ (1.National Research Institute for Earth Science and Disaster Resilience)

9:15 AM - 9:30 AM

[S24-02] Deep learning model for spatial interpolation of real-time seismic intensity

Ryota Otake^1,2, Jun Kurima², 〇Hiroyuki Goto², Sumio Sawada² (1.OC Global, 2.Kyoto University)

9:30 AM - 9:45 AM

[S24-03] False alarm and its measure on the earthquake early warning for the off-Torishima earthquake on July 30, 2020

〇Masumi Yamada¹ (1.DPRI Kyoto Univ.)

9:45 AM - 10:00 AM

[S24-04] Attempt to detect tectonic tremors in Sanriku-oki by applying a Convolutional Neural Network to S-net data

〇Keisuke Yano¹, Hidenobu Takahashi², Kazuya Tateiwa², Masayuki Kano² (1.The Institute of Statistical Mathematics, 2.Tohoku University)

10:00 AM - 10:15 AM

[S24-05] Development of 3D Convolutional Neural Network to locate hypocenter around Hakone with theoretical seismograms of time series as training data.

〇Daisuke Sugiyama¹, Seiji Tsuboi¹, Yukutake Yohei² (1.JAMSTEC, 2.Hot Springs Research Institute, Kanagawa Prefectural Government)

Poster session (Oct. 30th)

S24P

Fri. Oct 30, 2020 4:00 PM - 5:30 PM ROOM P

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4:00 PM - 5:30 PM

[S24P-01] Determination of P-wave first motion polarities and moment tensors for hydraulically-induced AEs using CNN technique

〇Rui Tanaka¹, Makoto Naoi¹, Youqing Chen¹, Kazune Yamamoto¹, Keiichi Imakita¹, Naofumi Tsutsumi¹, Akihiro Shimoda¹, Daiki Hiramatsu¹, Hironori Kawakata², Tsuyoshi Ishida¹, Eiichi Fukuyama¹, Hiroyuki Tanaka³, Yutaro Arima³, Shigehiro Kitamura³, Daisuke Hyodo³ (1.Kyoto University, 2.Ritsumeikan University, 3.JOGMEC)

4:00 PM - 5:30 PM

[S24P-02] An unsupervised automatic classification for continuous seismic records: introducing an anomaly detection algorithm to solve the imbalanced data problem

〇Yuki Kodera¹, Shin'ichi Sakai² (1.Meteorological Research Institute, Japan Meteorological Agency, 2.Earthquake Research Institute, the University of Tokyo)

4:00 PM - 5:30 PM

[S24P-03] Denoise and seismic phase detection with Convolutional Neural Network

〇Kengo Shimojo¹, Shouta Kudo¹, Yuriko Iwasaki¹, Koji Tamaribuchi² (1.Japan Meteorological Agency, 2.Meteorological Research Institute)

4:00 PM - 5:30 PM

[S24P-04] Detection of seismic signals under low SNR condition using an artificial neural network: Toward development of dense low cost citizen seismic network in Japan

〇Ahyi Kim¹, Hiroki Uematsu¹, Yuji Nakamura¹, Yuta Takahashi², Momoko Nakamura² (1.Yokohama City University, 2.Senshu University)

4:00 PM - 5:30 PM

[S24P-05] Development of neural network to locate hypocenter with theoretical seismograms of time series as learning data

〇Daisuke Sugiyama¹, Seiji Tsuboi¹ (1.JAMSTEC)

4:00 PM - 5:30 PM

[S24P-06] Numerical experiments towards detection of deep low-frequency tremors from seismic waveform images via convolutional neural network

〇Ryosuke Kaneko¹, Hiromichi Nagao^1,2, Shin-ichi Ito^1,2, Kazushige Obara², Hiroshi Tsuruoka² (1.Graduate School of Information Science and Technology, The University of Tokyo, 2.Earthquake Research Institute, The University of Tokyo)

4:00 PM - 5:30 PM

[S24P-07] Machine learning models for aftershock forecasting: Application to the 2016 Kumamoto earthquake sequence

〇Hanyuan Huang¹, Hiroe Miyake¹ (1.Earthquake Research Institute, University of Tokyo)

Sessions

Special session » S24. Pioneering the future of seismology with machine learning

[S24]AM-1

[S24-01] Regional characteristic of seismic activity inferred from unsupervised machine learning of F-net moment-tensor catalog

[S24-02] Deep learning model for spatial interpolation of real-time seismic intensity

[S24-03] False alarm and its measure on the earthquake early warning for the off-Torishima earthquake on July 30, 2020

[S24-04] Attempt to detect tectonic tremors in Sanriku-oki by applying a Convolutional Neural Network to S-net data

[S24-05] Development of 3D Convolutional Neural Network to locate hypocenter around Hakone with theoretical seismograms of time series as training data.

S24P

[S24P-01] Determination of P-wave first motion polarities and moment tensors for hydraulically-induced AEs using CNN technique

[S24P-02] An unsupervised automatic classification for continuous seismic records: introducing an anomaly detection algorithm to solve the imbalanced data problem

[S24P-03] Denoise and seismic phase detection with Convolutional Neural Network

[S24P-04] Detection of seismic signals under low SNR condition using an artificial neural network: Toward development of dense low cost citizen seismic network in Japan

[S24P-05] Development of neural network to locate hypocenter with theoretical seismograms of time series as learning data

[S24P-06] Numerical experiments towards detection of deep low-frequency tremors from seismic waveform images via convolutional neural network

[S24P-07] Machine learning models for aftershock forecasting: Application to the 2016 Kumamoto earthquake sequence

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