Room B

[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)

Poster session (3rd Day)

[S21P] PM-P

Wed. Oct 26, 2022 1:30 PM - 4:00 PM ROOM P-1 (10th floor (Conference Room 1010-1070))

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1:30 PM - 4:00 PM

[S21P-01] Automatic detection of S-wave later phase by 1D convolutional neural network

*Yuta Amezawa¹, Takahiko Uchide¹, Takahiro Shiina¹ (1. Geological Survey of Japan, National Institute of Advanced Industrial Science and Technology)

1:30 PM - 4:00 PM

[S21P-02] Event catalog development for AEs induced by hydraulic fracturing experiments in the laboratory

*Makoto NAOI¹, Youqing Chen¹, Yutaro Arima² (1. Kyoto University, 2. JOGMEC)

1:30 PM - 4:00 PM

[S21P-03] Towards Constructing a High-precision Phase picker for Volcanic Earthquakes Using Deep Learning: Performance Evaluation of Various Models

*Yuji Nakamura¹, Ahyi Kim¹, Hiroki Uematsu¹, Yohei Yukutake², Yuki Abe³ (1. Yokohama City University, 2. Earthquake Research Institute, University of Tokyo, 3. Hot Springs Research Institute of Kanagawa Prefecture)

1:30 PM - 4:00 PM

[S21P-04] Estimation of volcanic earthquakes at Kirishima volcano using machine learning

*Yohei YUKUTAKE¹, Ahyi KIM² (1. Earthquake Research Institute, The University of Tokyo, 2. Yokohama City University)

1:30 PM - 4:00 PM

[S21P-05] Development of Seismic Wave Detection System using Machine Learning for a Citizen Seismic Network

*Yukino Yazaki¹, Yuji Nakamura¹, Hiroki Uematsu^1,2, Ahyi Kim¹, Masami Yamasaki¹ (1. Yokohama City University, 2. National Institute of Informatics)

1:30 PM - 4:00 PM

[S21P-06] Toward Efficient Pore Pressure Estimation along Plate Boundary Faults Using Deep Learning

*Fan Yu¹, Ehsan Jamali Hondori², Jin-Oh Park¹ (1. Atmosphere and Ocean Research Institute, The University of Tokyo, 2. Geoscience Enterprise Inc.)

1:30 PM - 4:00 PM

[S21P-07] Fault Plane Estimation from 3D Hypocenter Distribution by Two-step Clustering Considering Local Shape

*Yoshihiro Sato¹, Haruo Horikawa¹, Takahiko Uchide¹, *Satoru Fukayama¹, Jun Ogata¹ (1. National Institute of Advanced Industrial Science and Technology (AIST))

1:30 PM - 4:00 PM

[S21P-08] Estimation of wave propagation direction from seismic waveforms using a deep learning model

*Yuki KODERA¹ (1. Meteorological Research Institute, Japan Meteorological Agency)

1:30 PM - 4:00 PM

[S21P-09] Towards Real-Time Seismic Intensity Prediction Using Deep Learning:Developing a Prediction Model with Relatively Little Observed Data

*Momoko Nakamura¹, Yuji Nakamura¹, Hiroki Uematsu¹, Yukino Yazaki¹, Ahyi Kim¹, Hisahiko Kubo² (1. Yokohama City University, 2. NIED)

1:30 PM - 4:00 PM

[S21P-10] Experiment on Early Prediction of Long-Period Earthquake Motions Based on Deep Learning

*Takashi FURUMURA¹ (1. Earthquake Research Institute, The University of Tokyo)

1:30 PM - 4:00 PM

[S21P-11] Study on prediction of pseudo velocity response spectrum by machine learning —Part3 Simulation of response spectrum of crustal earthquake in Eastern Japan—

*TIANZENG WEI¹, KAI TERAZONO¹, HIDENORI KAWABE¹ (1. Osaka University)

1:30 PM - 4:00 PM

[S21P-12] Study on Prediction of Pseudo-velocity Response Spectrum Using Machine Learning -Part2: Response Spectrum Simulation of the 2016 Kumamoto Earthquake-

*Kai Terazono¹, Tianzeng Wei¹, Hidenori Kawabe¹ (1. Osaka University)

Cancelled

1:30 PM - 4:00 PM

[S21P-13] Improvement of Single Station Algorithm in Earthquake Early Warning Using Automatic Optimization of Network Structure for Deep Learning

*Shunta Noda¹, Naoyasu Iwata¹ (1. Railway Technical Research Institute)

Sessions

Special session » S21. Advancing Seismology with AI

[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

[S21P] PM-P

[S21P-01] Automatic detection of S-wave later phase by 1D convolutional neural network

[S21P-02] Event catalog development for AEs induced by hydraulic fracturing experiments in the laboratory

[S21P-03] Towards Constructing a High-precision Phase picker for Volcanic Earthquakes Using Deep Learning: Performance Evaluation of Various Models

[S21P-04] Estimation of volcanic earthquakes at Kirishima volcano using machine learning

[S21P-05] Development of Seismic Wave Detection System using Machine Learning for a Citizen Seismic Network

[S21P-06] Toward Efficient Pore Pressure Estimation along Plate Boundary Faults Using Deep Learning

[S21P-07] Fault Plane Estimation from 3D Hypocenter Distribution by Two-step Clustering Considering Local Shape

[S21P-08] Estimation of wave propagation direction from seismic waveforms using a deep learning model

[S21P-09] Towards Real-Time Seismic Intensity Prediction Using Deep Learning:Developing a Prediction Model with Relatively Little Observed Data

[S21P-10] Experiment on Early Prediction of Long-Period Earthquake Motions Based on Deep Learning

[S21P-11] Study on prediction of pseudo velocity response spectrum by machine learning —Part3 Simulation of response spectrum of crustal earthquake in Eastern Japan—

[S21P-12] Study on Prediction of Pseudo-velocity Response Spectrum Using Machine Learning -Part2: Response Spectrum Simulation of the 2016 Kumamoto Earthquake-

[S21P-13] Improvement of Single Station Algorithm in Earthquake Early Warning Using Automatic Optimization of Network Structure for Deep Learning

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