[S-TT37] Seismic Big Data Analysis Based on the State-of-the-Art of Bayesian Statistics

Thu. Jun 3, 2021 5:15 PM - 6:30 PM Ch.14

convener:Hiromichi Nagao(Earthquake Research Institute, The University of Tokyo), Aitaro Kato(Earthquake Research Institute, the University of Tokyo), Keisuke Yano(The Institute of Statistical Mathematics), Takahiro Shiina(National Institute of Advanced Industrial Science and Technology)

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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5:15 PM - 6:30 PM

[STT37-P01] Clustering seismic noises using semi-supervised learning

*Kotaro Sato¹, Keisuke Yano², Komaki Fumiyasu¹ (1.The University of Tokyo, 2.The Institute of Statistical Mathematics)

5:15 PM - 6:30 PM

[STT37-P02] Convolutional Neural Network to Detect Deep Low-Frequency Tremors from Seismic Waveform Images

*Ryosuke Kaneko^1,2, Hiromichi Nagao^2,1, Shin-ichi Ito^2,1, Kazushige Obara², Hiroshi Tsuruoka² (1.Graduate School of Information Science and Technology, The University of Tokyo, 2.Earthquake Research Institute, The University of Tokyo)

5:15 PM - 6:30 PM

[STT37-P03] A Convolutional Neural Network based classification of local earthquakes and tectonic tremors in Sanriku-oki using a single station S-net data and its generalization ability

*Masayuki Kano¹, Hidenobu Takahashi¹, Kazuya Tateiwa¹, Keisuke Yano² (1.Graduate school of science, Tohoku University, 2.The Institute of Statistical Mathematics)

5:15 PM - 6:30 PM

[STT37-P04] Forecasting temporal variation of aftershocks immediately after a main shock using Gaussian process regression

*Kosuke Morikawa¹, Hiromichi Nagao^2,3, Shin-ichi Ito^2,3, Yoshikazu Terada^1,4, Shin'ichi Sakai^2,5, Naoshi Hirata^2,6 (1.Osaka University, 2.Earthquake Research Institute, The University of Tokyo,, 3.Graduate School of Information Science and Technology, 4.Center for Advanced Intelligence Project, 5.Interfaculty Initiative in Information Studies, The University of Tokyo, 6.National Research Institute for Earth Science and Disaster Resilience)

5:15 PM - 6:30 PM

[STT37-P05] Detection of deep low-frequency earthquakes based on matched filter technique using mutual information

*Ryo Kurihara¹, Aitaro Kato¹, Hiromichi Nagao^1,2, Sumito Kurata² (1.Earthquake Research Institute, The University of Tokyo, 2.Graduate School of Information Science and Technology, The University of Tokyo)

5:15 PM - 6:30 PM

[STT37-P06] Adjoint-based uncertainty quantification of frictional inhomogeneity on slow-slipping fault

*Shin-ichi Ito¹, Masayuki Kano², Hiromichi Nagao¹ (1.The University of Tokyo, 2.Tohoku University)

5:15 PM - 6:30 PM

[STT37-P07] Sensor selection for seismic wavefield reconstruction based on sparse observation (Part II: Preliminary study on method based on data-driven low-dimensional model)

*Takayuki Nagata¹, Kumi Nakai¹, Yuji Saito¹, Taku Nonomura¹, Masayuki Kano¹, Shin-ichi Ito², Hiromichi Nagao² (1.Tohoku University, 2.Earthquake Research Institute, The University of Tokyo)

Session information

[S-TT37] Seismic Big Data Analysis Based on the State-of-the-Art of Bayesian Statistics

[STT37-P01] Clustering seismic noises using semi-supervised learning

[STT37-P02] Convolutional Neural Network to Detect Deep Low-Frequency Tremors from Seismic Waveform Images

[STT37-P03] A Convolutional Neural Network based classification of local earthquakes and tectonic tremors in Sanriku-oki using a single station S-net data and its generalization ability

[STT37-P04] Forecasting temporal variation of aftershocks immediately after a main shock using Gaussian process regression

[STT37-P05] Detection of deep low-frequency earthquakes based on matched filter technique using mutual information

[STT37-P06] Adjoint-based uncertainty quantification of frictional inhomogeneity on slow-slipping fault

[STT37-P07] Sensor selection for seismic wavefield reconstruction based on sparse observation (Part II: Preliminary study on method based on data-driven low-dimensional model)

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