Japan Geoscience Union Meeting 2019

Session information

[J] Poster

S (Solid Earth Sciences ) » S-TT Technology & Techniques

[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.

*Masayuki Kano1, Hiromichi Nagao2,3, Kenji Nagata4,5, Shin-ichi Ito2,3, 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)

×

Authentication

Abstract password authentication.
Password is required to view the abstract. Please enter a password to authenticate.

×

Please log in with your participant account.
» Participant Log In