Solid Earth Sciences (S) | ||
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Session Sub-category | Technology & Techniques(TT) | |
Session ID | S-TT43 | |
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 |
Yoshikazu Terada (The University of Osaka) Hidetoshi Matsui (Shiga University) |
Time | Presentation No | Title | Presenter |
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Oral Presentation May 26 AM2 | |||
10:45 - 11:00 | STT43-01 | Slip-size-dependent Brownian passage time model with slip-size uncertainties | Yoshikazu Terada |
11:00 - 11:15 | STT43-02 | Extraction of Spatial Features of Seismic Intensity Based on Field Reconstruction: Towards Constraining the Source Locations of Historical Earthquakes | Motoko Ishise |
11:15 - 11:30 | STT43-03 | An introduction of the MCMC method to a Joint Hypocenter Determination problem: Implications for an approximate JHD method | Takahiro Shiina |
11:30 - 11:45 | STT43-04 | Ambient Noise Missing Data Prediction Using Long Short Time Memory (LSTM) in the United Arab Emirates (UAE) | Intan Andriani Putri |
11:45 - 12:00 | STT43-05 | Acquisition of a Stochastic Differential Equation Representation for Slow Earthquakes via Deep Learning to Deepen Phenomenological Understanding | Toshiro Kusui |
12:00 - 12:15 | STT43-06 |
Site selection based on Bayesian sensitivity for earthquake early warning |
Keisuke Yano |
Presentation No | Title | Presenter |
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Poster Presentation May 26 PM3 | ||
STT43-P01 | Creation of training and validation datasets of Distributed Acoustic Sensing recordings from Sanriku seafloor observation system, Japan. | Gerardo Manuel Mendo Perez |
STT43-P02 | Seismic detection based on combination of station-wise phase picks by deep learning and application to dense seismic observation network data | Tokuda Tomoki |
STT43-P03 | Kriging for functional data via sparse regularization | Hidetoshi Matsui |
STT43-P04 | PoViT-UQ : P-wave Polarity and Arrival Time Determination using Vision Transformer with Uncertainty Quantification | Shinya Katoh |
STT43-P05 | Gaussian Process Model for Spatio-temporal Background Seismicity Rates | Yuanyuan Niu |