Session information

[E] Oral

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

[S-TT51] State-of-the-Art Seismic Data Analysis Based on Bayesian Statistics

Thu. May 28, 2026 10:45 AM - 12:15 PM Exhibition Hall Special Setting (8) (Exhibition Hall 7&8, Makuhari Messe)

Chairperson:Nagao Hiromichi(Earthquake Research Institute, The University of Tokyo), Kato Aitaro(Earthquake Research Institute, the University of Tokyo), Yano Keisuke(The Institute of Statistical Mathematics), Sato Daisuke(Japan Agency for Marine-Earth Science and Technology)

Bayesian statistics forms the mathematical foundation of various data anaysis methods, such as machine learning including artificial intelligence. This session mainly accepts presentations that focus on seismic data analysis, especially related to analysis methods based on Bayesian statistics such as machine learning, deep learning 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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10:45 AM - 11:00 AM

[STT51-01] Deep Generative Modeling of Ground Motions toward Integrated Seismic Hazard and Risk Analysis

★Invited Papers

*Itoi Tatsuya¹, Matsumoto Yuma², Keiichi Ishikawa¹, Sangwon Lee¹, Taro Yaoyama¹ (1. The University of Tokyo, 2. National Research Institute for Earth Science and Disaster Resilience)

11:00 AM - 11:15 AM

[STT51-02] Application of a fine-tuned BERT model for seafloor DAS data processing

*Gerardo Manuel Mendo Perez¹, Hiromichi Nagao^1,2,3, Shinya Katoh¹, Masanao Shinohara¹ (1. Earthquake Research Institute, The University of Tokyo, 2. Graduate School of Information Science and Technology, The University of Tokyo, 3. RIKEN Center for Advanced Intelligence Project)

11:15 AM - 11:30 AM

[STT51-03] Benchmarking Deep Learning Architectures for Rapid Disaster Response: A Case Study of the 2024 Noto Peninsula Earthquake Using Multi-Source Remote Sensing and Open Damage Inventories

*Minhaz Chowdhury¹, Md Shofiqul Islam¹ (1. Shahjalal University of Science and Technology, Sylhet)

11:30 AM - 11:45 AM

[STT51-04] Oscillator decomposition of earthquake data

★Invited Papers

*Takeru Matsuda^1,2, Satoru Baba³, Akiko Takeo¹, Aitaro Kato¹ (1. University of Tokyo, 2. RIKEN Center for Brain Science, 3. Kyushu University)

11:45 AM - 12:00 PM

[STT51-05] Statistical Seismicity Models Incorporating the Effects of Slow Earthquakes

★Invited Papers

*Tomoaki Nishikawa¹ (1. Disaster Prevention Research Institute, Kyoto University)

12:00 PM - 12:15 PM

[STT51-06] Gaussian Process Modeling of Spatio-temporal Background Seismicity

*Yuanyuan Niu¹, Jiancang Zhuang^1,2 (1. The Institute of Statistical Mathematics, 2. The Graduate University for Advanced Studies, SOKENDAI. )

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