Session Details
[S21P]PM-P
Wed. Oct 26, 2022 1:30 PM - 4:00 PM JST
Wed. Oct 26, 2022 4:30 AM - 7:00 AM UTC
Wed. Oct 26, 2022 4:30 AM - 7:00 AM UTC
ROOM P-1 10th floor (Conference Room 1010-1070)
[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)
[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)
[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)
[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)
[S21P-05]Development of Seismic Wave Detection System using Machine Learning for a Citizen Seismic Network
*Yukino Yazaki1, Yuji Nakamura1, Hiroki Uematsu1,2, Ahyi Kim1, Masami Yamasaki1 (1. Yokohama City University, 2. National Institute of Informatics)
[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.)
[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))
[S21P-08]Estimation of wave propagation direction from seismic waveforms using a deep learning model
*Yuki KODERA1 (1. Meteorological Research Institute, Japan Meteorological Agency)
[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)
[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)
[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)
[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)