[S21] AM-1

Tue. Oct 25, 2022 10:00 AM - 11:30 AM ROOM B (4th floor (Large Conference Room))

chairperson:Hisahiko Kubo(NIED), Makoto Naoi(DPRI, Kyoto Univ.), Tomohisa OKAZAKI(RIKEN AIP)

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10:00 AM - 10:15 AM

[S21-01] [Invited]Detecting slow slip events and accompanying tectonic tremor in geodetic and seismic data, using machine learning

*Bertrand ROUET-LEDUC¹ (1. DPRI, Kyoto University)

10:15 AM - 10:30 AM

[S21-02] Detection of Deep Low-Frequency Tremors from Continuous Paper Records at a Station in Southwest Japan About 50 Years Ago Based on Convolutional Neural Network for Seismogram Images

Ryosuke Kaneko^2,1, *Hiromichi NAGAO^1,2, Shin-ichi Ito^1,2, Hiroshi Tsuruoka¹, Kazushige Obara¹ (1. Earthquake Research Institute, The University of Tokyo, 2. Graduate School of Information Science and Technology, The University of Tokyo)

10:30 AM - 10:45 AM

[S21-03] Application of Physics-Informed Neural Networks to spring-slider model

*Rikuto Fukushima¹, Masayuki Kano², Kazuro Hirahara^3,4 (1. Kyoto University Faculty of Science, 2. Tohoku University Faculty of Science, 3. RIKEN, 4. Kagawa University)

10:45 AM - 11:00 AM

[S21-04] Optimal Transport Distance Measure in Geophysical Inversion

*Tomohisa OKAZAKI¹, Naonori Ueda¹ (1. RIKEN AIP)

11:00 AM - 11:15 AM

[S21-05] Study on prediction of pseudo velocity response spectrum by machine learning
-Part1 Response spectrum simulation for crustal earthquake in Kinki district, Japan-

*Hidenori KAWABE¹, Tianzeng WEI¹, Kai TERAZONO¹ (1. Osaka University)

11:15 AM - 11:30 AM

[S21-06] Prediction of velocity response spectrum by using a 1-D convolutional neural network

*Daiki Taya¹, Takashi Furumura¹ (1. Earthquake Research Institute, University of Tokyo)

Session information

[S21] AM-1

[S21-01] [Invited]Detecting slow slip events and accompanying tectonic tremor in geodetic and seismic data, using machine learning

[S21-02] Detection of Deep Low-Frequency Tremors from Continuous Paper Records at a Station in Southwest Japan About 50 Years Ago Based on Convolutional Neural Network for Seismogram Images

[S21-03] Application of Physics-Informed Neural Networks to spring-slider model

[S21-04] Optimal Transport Distance Measure in Geophysical Inversion

[S21-05] Study on prediction of pseudo velocity response spectrum by machine learning
-Part1 Response spectrum simulation for crustal earthquake in Kinki district, Japan-

[S21-06] Prediction of velocity response spectrum by using a 1-D convolutional neural network

Authentication

Session information

[S21] AM-1

[S21-01] [Invited]Detecting slow slip events and accompanying tectonic tremor in geodetic and seismic data, using machine learning

[S21-02] Detection of Deep Low-Frequency Tremors from Continuous Paper Records at a Station in Southwest Japan About 50 Years Ago Based on Convolutional Neural Network for Seismogram Images

[S21-03] Application of Physics-Informed Neural Networks to spring-slider model

[S21-04] Optimal Transport Distance Measure in Geophysical Inversion

[S21-05] Study on prediction of pseudo velocity response spectrum by machine learning -Part1 Response spectrum simulation for crustal earthquake in Kinki district, Japan-

[S21-06] Prediction of velocity response spectrum by using a 1-D convolutional neural network

Authentication

[S21-05] Study on prediction of pseudo velocity response spectrum by machine learning
-Part1 Response spectrum simulation for crustal earthquake in Kinki district, Japan-