Solid Earth Sciences (S)
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
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.
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
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
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