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)

Session information

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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