S-TT46

Solid Earth Sciences (S)
Session Sub-category		Technology & Techniques(TT)
Session ID		S-TT46
Title		Seismic Big Data Analysis Based on the State-of-the-Art of Bayesian Statistics
Short Title		Bayesian Analysis of Seismic Big Data
Main Convener	Name	Hiromichi Nagao
Main Convener	Affiliation	Earthquake Research Institute, The University of Tokyo
Co-Convener 1	Name	Aitaro Kato
Co-Convener 1	Affiliation	Earthquake Research Institute, the University of Tokyo
Co-Convener 2	Name	Takuto Maeda
Co-Convener 2	Affiliation	Graduate School of Science and Technology, Hirosaki University
Co-Convener 3	Name	Keisuke Yano
Co-Convener 3	Affiliation	The university of Tokyo
Session Language		J
Scope		Recently, a big seismic database has been being constructed that collects data of vibrators implemented in such as buildings, lifelines and smartphones, in addition to seismic data of the conventional continuous/temporal dense seismic observation arrays. Development of methodologies and algorithms, which are inadequate at this moment, optimized to comprehensively analyze the seismic big data is essential in order to utilize the big database as much as possible for prevention/mitigation of earthquake disasters and clarification of earthquake phenomena. On the other hand, recent progress of Bayesian statistics is significant, which is the mathematical basis of various methodologies, such as machine learning, especially deep learning, to extract valuable information from big data. The state-of-the-art of Bayesian statistics is expected to substantially advance seismic big data analyses. This session mainly accepts presentations that focus on analyses of seismic big data, especially related to analysis methods based on Bayesian statistics such as machine learning, sparse modeling and data assimilation, and their applications to real seismic data. Presentations related to mathematical or statistical theories beneficial to data analyses, feasibility studies of algorithms eventually applicable to real seismic data, and the current status of seismic observations and analysis results are also highly welcome.
Presentation Format		Oral and Poster presentation
Invited Authors		Yukitoshi Fukahata (Disaster Prevention Research Institute, Kyoto University) Takahiro Omi (Institue of Industrial Science, the University of Tokyo)

Time	Presentation No	Title	Presenter
Oral Presentation May 27 PM1
13:45 - 14:00	STT46-01	A bad inversion result suggests bad modeling	Yukitoshi Fukahata
14:00 - 14:15	STT46-02	A seismic wavefield estimation in a dense seismograph network: An optimization for the MeSO-net	Takahiro Shiina
14:15 - 14:30	STT46-03	Uncertainty quantification based on 4DVar data assimilation for massive simulation models	Shin-ichi Ito
14:30 - 14:45	STT46-04	Bayesian approach for statistical modeling and forecasting of aftershock activities	Takahiro Omi
14:45 - 15:00	STT46-05	Bias Correction for the Distribution of Aftershocks Within Short-Term Period Immediately After Large Main Shock	Kosuke Morikawa
15:00 - 15:15	STT46-06	Deep-learning-based Earthquake Detection for Continuous Seismic Network Records	Keisuke Yano

Presentation No	Title	Presenter
Poster Presentation May 27 PM2
STT46-P01	P-wave Polarity Determination of Waveform Data Observed in Western Japan, Using Deep Learning	Shota Hara
STT46-P02	Trans-dimensional Bayesian inversion for a radially anisotropic S-wave velocity model in the crust and upper mantle: Application to the Australian continent	Toru Taira
STT46-P03	Seismic wavefield imaging based on dense seismic networks using replica exchange Monte Carlo method	Masayuki Kano
STT46-P04	Bayesian oscillator decomposition for seismic data	Takeru Matsuda
STT46-P05	Uncertainty evaluation of source parameter estimates by MCMC in Oklahoma	Nana Yoshimitsu

JpGU Meeting 2019