JpGU Meeting 2025
Session Outline| Solid Earth Sciences (S) | ||||
|---|---|---|---|---|
| Session Sub-category | Technology & Techniques (TT) | |||
| Session ID | S-TT43 | |||
| Session Title | Seismic Big Data Analysis Based on the State-of-the-Art of Bayesian Statistics | |||
| Short Title | Bayesian Analysis of Seismic Big Data | |||
| Date & Time | Oral Session |
AM2 Mon, 26 MAY | ||
| On-site Poster Coretime |
PM3 Mon. 26 MAY | |||
| Main Convener | Name | Hiromichi Nagao | ||
| Affiliation | Earthquake Research Institute, The University of Tokyo | |||
| Co-Convener 1 | Name | Aitaro Kato | ||
| Affiliation | Earthquake Research Institute, the University of Tokyo | |||
| Co-Convener 2 | Name | Keisuke Yano | ||
| Affiliation | The Institute of Statistical Mathematics | |||
| Co-Convener 3 | Name | Takahiro Shiina | ||
| Affiliation | National Institute of Advanced Industrial Science and Technology | |||
| Session Language | J | |||
| Scope (Session Description) |
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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| Session Format | Orals and Posters session | |||
| Co-sponsorship | Partner Union(s) | - | ||
| JpGU Society Member(s) | - | |||
| International Collaborative Society | - | |||
| Organizations Other Than JpGU Society Members | - | |||