Room B

[S21] AM-1

Thu. Nov 2, 2023 9:45 AM - 10:30 AM Room B (F201)

chairperson:Takeo Ishibe

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9:45 AM - 10:00 AM

[S21-01] Machine learning-based hypocenter determination using seismic intensity data

*Takeo ISHIBE^1,2, Yoko Ogawa³, Hiroto Tanaka³, Ryota Kiuchi³, Tsutomu Takahama³, Kazuo Sakamoto³, Yusuke Saijo³, Mitsuko Furumura¹, Ritsuko S. Matsu'ura¹ (1. Association for the Development of Earthquake Prediction (ADEP) , 2. The Institute of Statistical Mathematics, 3. Kozo Keikaku Engineering Inc.)

10:00 AM - 10:15 AM

[S21-02] Deep Learning Estimating of Epicentral Distance for Earthquake Early Warning Systems

*Shunta NODA¹ (1. Railway Technical Research Institute)

10:15 AM - 10:30 AM

[S21-03] Application of Convolutional Neural Networks for Seismic Velocity Model Building Using “Realistic” Synthetic Data

*Fan Yu¹, Jamali Hondori Jamali Hondori², Jin-Oh Park¹ (1. the University of Tokyo, 2. Geoscience Enterprise Inc.)

Room B

[S21] AM-2

Thu. Nov 2, 2023 10:45 AM - 12:00 PM Room B (F201)

chairperson:Keisuke Yano, Yusuke Tanaka(Tohoku University)

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10:45 AM - 11:00 AM

[S21-04] Application of Watanabe’s Bayesian information criterion to the GNSS-A seafloor geodetic observation

*Shun-ichi WATANEBE¹, Tadashi Ishikawa¹, Yuto Nakamura¹, Koya Nagae¹, Yusuke Yokota² (1. Hydrographic and Oceanographic Department, Japan Coast Guard, 2. Institute of Industrial Science, University of Tokyo)

11:00 AM - 11:15 AM

[S21-05] Identifying crustal brocks using objective clustering of GNSS velocities via parallel translation and Euler vector

*Keisuke YANO¹, Atsushi Takahashi², Masayuki Kano³ (1. The Institute of Statistical Mathematics, 2. Center for Advance Intelligence Project, RIKEN, 3. Graduate School of Science, Tohoku University)

11:15 AM - 11:30 AM

[S21-06] Trial application of machine-learning-based phase detection to slow slip event signal in GNSS displacement time series

*Yusuke TANAKA¹, Masayuki Kano¹ (1. Graduate School of Science, Tohoku University)

11:30 AM - 11:45 AM

[S21-07] Challenges of applying deep learning to InSAR data for the purpose of detecting small deformation around active faults and volcanoes in Japan

*Yo FUKUSHIMA¹ (1. International Research Institute of Disaster Science, Tohoku University)

11:45 AM - 12:00 PM

[S21-08] Development of an Interpretable Volcanic Earthquake Classification Method Using Transformer Encoders

*Yugo Suzuki¹, Ahyi Kim¹, Masami Yamasaki¹ (1. Yokohama City University)

Room B

[S21] PM-1

Thu. Nov 2, 2023 1:15 PM - 2:45 PM Room B (F201)

chairperson:Ryoichiro Agata(JAMSTEC), Shinya Katoh

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1:15 PM - 1:35 PM

[S21-09] [Invited]Theoretical challenges to understand the principles of deep learning and artificial intelligence

*Masaaki Imaizumi¹ (1. Graduate School of Arts and Science, the University of Tokyo)

1:35 PM - 1:55 PM

[S21-10] [Invited]Potential pathway to understanding earthquakes using a data-driven approach: learning from other informatics applications

*Stephen Wu¹ (1. The Institute of Statistical Mathematics)

1:55 PM - 2:15 PM

[S21-11] [Invited]Deep-Learning Seismology: Current Status and Expectations for Future Developments

*Kazuro HIRAHARA^1,2 (1. Riken Center for Advanced Intelligence Project, 2. Kagawa University)

2:15 PM - 2:30 PM

[S21-12] Simultaneous estimation of fault geometry and slip distribution from crustal deformation data using deep operator learning

*Tomohisa OKAZAKI¹, Kazuro Hirahara¹, Naonori Ueda¹ (1. RIKEN Center for Advanced Intelligence Project)

2:30 PM - 2:45 PM

[S21-13] Physics-Informed Neural Networks for monitoring slow slip events in a spring-slider system

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

Room B

[S21] PM-2

Thu. Nov 2, 2023 3:00 PM - 4:15 PM Room B (F201)

chairperson:Kodai Sagae, Kosuke Morikawa(Osaka University)

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3:00 PM - 3:15 PM

[S21-14] Two-stage approach for transfer learning of seismic-phase detection model to a small sample size data via multiple clustering

*Tomoki Tokuda¹, Hiromichi Nagao^1,2 (1. ERI, The University of Tokyo, 2. Graduate School of Information Science and Technology, The University of Tokyo)

3:15 PM - 3:30 PM

[S21-15] ETN Detector by Convolution Neural Network Using All S-net Stations

*Kodai SAGAE¹, Masayuki KANO², Takahiko UCHIDE¹ (1. Geological Survey of Japan, AIST, 2. Graduate School of Science, Tohoku University)

3:30 PM - 3:45 PM

[S21-16] Development of a method for determining the hypocenter of tectonic tremors using Score-CAM

*Amane Sugii¹, Yoshihiro Hiramatsu¹, Takahiko Uchide², Kazutoshi Imanishi² (1. Kanazawa University, 2. National Institute of Advanced Industrial Science and Technology)

3:45 PM - 4:00 PM

[S21-17] Self-Excited Detection Probability: Applications to Estimating Time-Variant b-Value Functions Immediately After Main Shocks

*Kosuke Morikawa¹, Hiromichi Nagao², Naoshi Hirata^3,2 (1. Osaka University, 2. Univ. of Tokyo, 3. National Research Institute for Earth Science and Disaster Resilience)

4:00 PM - 4:15 PM

[S21-18] Detection of changes in global background and clustering seismicity

*Jiancang ZHUANG^1,2, Ziyao Xiong³ (1. The Institute of Statistical Mathematics, 2. Graduate Institute for Advanced Studies, SOKENDAI , 3. Institute of Geology, China Earthquake Administration)

Poster session (Sept. 17th)

[S21P] PM-P

Wed. Nov 1, 2023 5:00 PM - 6:30 PM Room P3 (F205 and 6 side foyer) (Hall Annex)

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[S21P-01] On the effects of time width used to count interval maximum amplitude of a seismogram on the extreme value analysis

*Kaoru SAWAZAKI¹ (1. National Research Institute for Earth Science and Disaster Resilience)

[S21P-02] AE Catalog development by a deep hashing method and network correlation analysis

*Makoto NAOI¹, Shiro HIRANO², Youqing CHEN¹ (1. Kyoto University, 2. Ritsumeikan University)

[S21P-03] Attempt to Predict Response Spectra from Fourier Spectra Using Machine Learning

*Yuki Kurata¹, Hiroaki Matsukawa¹, Hiroto Tanaka¹, Yasuhiro Matsumoto¹, Mika Takekoshi¹, Takachika Yagi¹, Ryota Kiuchi¹, Yuta Taki¹ (1. KOZO KEIKAKU ENGINEERING Inc.)

[S21P-04] Predicting the probability of seismic motion exceedance based on short-term earthquake probability information after a large earthquake

*Hisahiko KUBO¹, Katsuhiko SHIOMI¹ (1. National Research Institute for Earth Science and Disaster Resilience)

[S21P-05] Comprehensive detection of later phase in S coda throughout the inland Japanese archipelago with deep learning

*Yuta AMEZAWA¹, Takahiko Uchide¹, Takahiro Shiina¹, Jun Ogata², Satoru Fukayama², Hiroki Kuroda³ (1. Geological Survey of Japan, National Institute of Advanced Industrial Science and Technology, 2. Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology , 3. Department of Information and Management Systems Engineering, Nagaoka University of Technology)

[S21P-06] Extraction of deep tectonic tremor waveforms using deep learning: Application to Hi-net stations in the Nankai subduction zone

*Yuya Jinde¹, Amane Sugii¹, Yoshihiro Hiramatsu¹ (1. Kanazawa University)

[S21P-07] You Only Search Once: An alternative framework to detect slow earthquakes.

*GERARDO MANUEL MENDO PEREZ¹, HIROMICHI NAGAO¹ (1. Earthquake Research Institute, The University of Tokyo)

[S21P-08] Which is more suitable for polarity determination using deep learning, time series data or image data?

*Shinya KATOH¹, Yoshihisa IIO², Hiroshi KATAO², Masayo SAWADA², Kazuhide TOMISAKA² (1. Earthquake Research Institute,The University of Tokyo, 2. Disaster Prevention Research Institute Kyoto University)

[S21P-09] Fast Smoothing Method for Spatiotemporal Data and Its Application to Seismic Wavefield Reconstruction

*Yoshikazu Terada^1,2, Hidetoshi Matsui³, Hiromichi Nagao⁴ (1. Osaka Univ., 2. RIKEN, 3. Shiga Univ., 4. Earthquake Research Institute, Univ. of Tokyo)

[S21P-10] Hypocenter-based Visualization of Multiscale Earthquake Faults:
Application to Crustal Earthquakes in Japan Islands

*Yasunori SAWAKI¹, Yoshihiro SATO^2,3, Takahiko UCHIDE¹, Haruo HORIKAWA¹, Takahiro SHIINA¹, Yuta AMEZAWA¹ (1. Geological Survey of Japan, AIST, 2. Tokyo City University, 3. Artificial Intelligence Research Center, AIST)

[S21P-11] Construction of a high-performance volcanic earthquake phase detection model using deep learning and examination of its transfer learning

*Yuji Nakamura^1,2, Ahyi Kim¹, Yohei Yukutake³, Yuki Abe⁴ (1. Yokohama City University, 2. activecore, Inc., 3. Earthquake Research Institute, University of Tokyo, 4. Hot Springs Research Institute of Kanagawa Prefecture)

[S21P-12] Machine-learning model of GNSS displacement rate estimation using seismic waveforms in western Shikoku

*Kazuki OHTAKE¹, Aitaro KATO¹, Yutaro OKADA², Takuya NISHIMURA³ (1. Earthquake Research Institute, The University of Tokyo, 2. Graduate School of Science, Kyoto University, 3. Disaster Prevention Research Institute, Kyoto University)

[S21P-13] Evaluation of GNSS-A positioning accuracy using a simple numerical model and the PyMC library

*Yuto NAKAMURA¹, Tadashi Ishikawa¹, Yusuke Yokota², Shun-ichi Watanabe¹, Koya Nagae¹ (1. Hydrographic and Oceanographic Department, Japan Coast Guard, 2. Institute of Industrial Science, University of Tokyo)

[S21P-14] Acoustic waveform analysis for GNSS-Acoustic observation using Machine Learning

*Naoki Kurosu¹, Motoyuki Kido² (1. Graduate School of Science, Tohoku University, 2. International Research Institute for Disaster Science, Tohoku University)

[S21P-15] Development of a Seismic Phase Pick 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)

Sessions

Special session » S21. Acceleration of seismological research through integration with information science

[S21] AM-1

[S21] AM-2

[S21] PM-1

[S21] PM-2

[S21P] PM-P

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