Poster session (Sept. 17th)

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

[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)

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