Bayesian oscillator decomposition for seismic data

Takeru Matsuda

Presentation information

[J] Poster

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

[S-TT46] Seismic Big Data Analysis Based on the State-of-the-Art of Bayesian Statistics

Mon. May 27, 2019 3:30 PM - 5:00 PM Poster Hall (International Exhibition Hall8, Makuhari Messe)

convener:Hiromichi Nagao(Earthquake Research Institute, The University of Tokyo), Aitaro Kato(Earthquake Research Institute, the University of Tokyo), Takuto Maeda(Graduate School of Science and Technology, Hirosaki University), Keisuke Yano(The university of Tokyo)

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[STT46-P04] Bayesian oscillator decomposition for seismic data

*Takeru Matsuda¹ (1.The University of Tokyo)

Keywords:Bayesian statistics, state space model, data assimilation

Many time series including seismic data are naturally considered as a superposition of several oscillators. Matsuda and Komaki (2017a,b) proposed a Bayesian statistical method for decomposing time series data into oscillators by using Gaussian linear state space models. For example, this method can be used to extract neural oscillators (such as alpha, beta, and gamma) from neuroimaging data. In this study, we apply this method to seismic data and investigate the extracted oscillators.