[S-TT46] Seismic Big Data Analysis Based on the State-of-the-Art of Bayesian Statistics

Mon. May 27, 2019 5:15 PM - 6:30 PM Poster Hall (International Exhibition Hall8, Makuhari Messe)

convener:Hiromichi Nagao(Earthquake Research Institute, The University of Tokyo), Aitaro Kato(Earthquake Research Institute, the University of Tokyo), Takuto Maeda(Graduate School of Science and Technology, Hirosaki University), Keisuke Yano(The university of Tokyo)

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.

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[STT46-P01] P-wave Polarity Determination of Waveform Data Observed in Western Japan, Using Deep Learning

*Shota Hara¹, Yukitoshi Fukahata², Yoshihisa Iio² (1.Division of Earth and Planetary Sciences, Graduate School of Science, Kyoto University, 2.Disaster Prevention Research Institute, Kyoto University)

[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 Taira¹, Kazunori Yoshizawa² (1.Graduate School of Science, Hokkaido University, 2.Faculty of Science, Hokkaido University)

[STT46-P03] Seismic wavefield imaging based on dense seismic networks using replica exchange Monte Carlo method

*Masayuki Kano¹, Hiromichi Nagao^2,3, Kenji Nagata^4,5, Shin-ichi Ito^2,3, Kei Hasegawa², Shin'ichi Sakai², Shigeki Nakagawa², Muneo Hori², Naoshi Hirata² (1.Graduate school of science, Tohoku University, 2.Earthquake Research Institute, The University of Tokyo, 3.Graduate School of Information Science and Technology, The University of Tokyo, 4.AIST, 5.Japan Science and Technology Agency)

[STT46-P04] Bayesian oscillator decomposition for seismic data

*Takeru Matsuda¹ (1.The University of Tokyo)

[STT46-P05] Uncertainty evaluation of source parameter estimates by MCMC in Oklahoma

*Nana Yoshimitsu¹, Takuto Maeda², William Ellsworth³ (1.The University of Tokyo, Earthquake Research Institute, 2.Hirosaki University, 3.Stanford Univerisy)