Solid Earth Sciences (S) | ||
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Session Sub-category | Technology & Techniques(TT) | |
Session ID | S-TT38 | |
Title | Seismic Big Data Analysis Based on the State-of-the-Art of Bayesian Statistics | |
Short Title | Bayesian Analysis of Seismic Big Data | |
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 |
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Scope |
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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Presentation Format | Oral and Poster presentation | |
Invited Authors |
Akemi Noda (Meteorological Research Institute, Japan Meteorological Agency) Kosuke Morikawa (Osaka University) |
Time | Presentation No | Title | Presenter |
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Oral Presentation May 27 PM1 | |||
13:45 - 14:00 | STT38-01 | Objective estimation of deformation structure in the crust based on inversion of moment density tensor | Akemi Noda |
14:00 - 14:15 | STT38-02 | Development of a Deep Low-Frequency Earthquake Detection Method Using Deep Learning with Multiple Traces as Inputs | Shinya Katoh |
14:15 - 14:30 | STT38-03 | Various field data applications of cSPM analysis for comprehensive evaluation of 3D particle motion | Yusuke Mukuhira |
14:30 - 14:45 | STT38-04 | Early aftershock activity estimation by integrating seismic waveforms and earthquake catalog | Kosuke Morikawa |
14:45 - 15:00 | STT38-05 | Bayesian non-parametric inference for the ETAS model | Yuanyuan Niu |
Presentation No | Title | Presenter |
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Poster Presentation May 27 PM3 | ||
STT38-P01 | Two-stage approach for transfer learning of seismic-phase detection model to small sample size data | Tokuda Tomoki |
STT38-P02 | Objective clustering of GNSS velocities based on parallel translation and Euler-vector estimation for the identification of crustal blocks | Keisuke Yano |
STT38-P03 | Creating slow earthquake template catalogs with You Only Search Once algorithm | Gerardo Manuel Mendo Perez |
STT38-P04 | Characterizing Spatial Distribution of Seismic Intensity to Constrain Hypocenters of Historical Earthquakes | Motoko Ishise |
STT38-P05 | Event Detection and Classification from Seismic Waveform Data Based on Optimal Transport Theory | Hiromichi Nagao |
STT38-P06 | Hessian-based uncertainty quantification for Physics-Informed Neural Networks: Application to the frictional parameter estimation | Rikuto Fukushima |
STT38-P07 | Acquisition of stochastic differential equation representations to characterize low-frequency tremors in seismic waveform data using deep learning | Toshiro Kusui |