S22P

Wed. Sep 18, 2019 1:00 PM - 2:30 PM ROOM P (International Conference Halls II and III)

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1:00 PM - 2:30 PM

[S22P-01] Detection of seismic signals under low SNR condition using an artificial neural network: Toward the development of low cost seismic network

Ahyi Kim¹, *Hiroki Uematsu¹ (1. Yokohama City University)

1:00 PM - 2:30 PM

[S22P-02] Earthquake/Noise Discrimination with Convolutional Neural Network

*Shota Kudo¹, Kengo Shimojo¹, Koji Tamaribuchi² (1. Japan Meteorological Agency, 2. Meteorological Research Institute)

1:00 PM - 2:30 PM

[S22P-03] Development of neural network to locate hypocenter with theoretical seismograms as learning data II

*Seiji Tsuboi¹, Daisuke Sugiyama¹ (1. Japan Agency for Marine-Earth Science and Technology)

1:00 PM - 2:30 PM

[S22P-04] Attempt to detect LFTs by clustering short-period OBS records

*Hidenobu Takahashi¹, Ryota Hino¹, Yusaku Ohta¹, Naoki Uchida¹, Syuichi Suzuki¹, Masanao Shinohara² (1. Tohoku University, 2. The University of Tokyo)

1:00 PM - 2:30 PM

[S22P-05] Insight into machine-learning model and prediction result: Case of random forest predictor for ground-motion intensit

*Hisahiko Kubo¹, Takashi Kunugi¹, Wataru Suzuki¹, Takeshi Kimura¹, Shin Aoi¹ (1. National Research Institute for Earth Science and Disaster Resilience)

1:00 PM - 2:30 PM

[S22P-06] site2vec: A Ground-Motion Predictor Learning Site Conditions from Data

*Tomohisa Okazaki¹, Tomoharu Iwata^1,2, Asako Iwaki³, Hiroyuki Fujiwara³, Naonori Ueda¹ (1. RIKEN Center for Advanced Intelligence Project, 2. NTT Communication Science Laboratories, 3. National Research Institute for Earth Science and Disaster Resilience)

1:00 PM - 2:30 PM

[S22P-07] Time series analyses of amplitude of the Earth's background free oscillations

*Ryuichi Kunugi¹, Naoki Suda¹ (1. Department of Earth and Planetary Systems Science, Graduate School of Science, Hiroshima University)

1:00 PM - 2:30 PM

[S22P-08] An Attempt to Classify a Pattern of Changes in Strain Observation Data by Machine Learning

*Takahiro Tsuyuki¹ (1. Meteorological Research Institute, Japan Meteorological Agency)

1:00 PM - 2:30 PM

[S22P-09] Machine Learning Approach for Integration of Multiple Relative Intensity Models

*Hirotaka Hachiya^1,2, Kazuro Hirahara¹, Naonori Ueda¹ (1. Center for Advanced Intelligence Project, RIKEN, 2. Faculty of System Engineering, Wakayama University)

1:00 PM - 2:30 PM

[S22P-10] Optimal distribution of offshore observation points for tsunami prediction using scenario search and regression methods

*Toshitaka Baba¹, Takuya Miyashita², Nobuhito Mori², Kenta Nakanishi³, Masato Okada⁴ (1. Tokushima University, 2. Kyoto University, 3. NITA Consultant, 4. Univ. of Tokyo)

Session information

S22P

[S22P-01] Detection of seismic signals under low SNR condition using an artificial neural network: Toward the development of low cost seismic network

[S22P-02] Earthquake/Noise Discrimination with Convolutional Neural Network

[S22P-03] Development of neural network to locate hypocenter with theoretical seismograms as learning data II

[S22P-04] Attempt to detect LFTs by clustering short-period OBS records

[S22P-05] Insight into machine-learning model and prediction result: Case of random forest predictor for ground-motion intensit

[S22P-06] site2vec: A Ground-Motion Predictor Learning Site Conditions from Data

[S22P-07] Time series analyses of amplitude of the Earth's background free oscillations

[S22P-08] An Attempt to Classify a Pattern of Changes in Strain Observation Data by Machine Learning

[S22P-09] Machine Learning Approach for Integration of Multiple Relative Intensity Models

[S22P-10] Optimal distribution of offshore observation points for tsunami prediction using scenario search and regression methods

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