Japan Geoscience Union Meeting 2021

Presentation information

Print

[J] Oral

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

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

Thu. Jun 3, 2021 10:45 AM - 12:15 PM Ch.18 (Zoom Room 18)

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), Chairperson:Ryo Kurihara(Earthquake Research Institute, The University of Tokyo), Takayuki Nagata(Tohoku University)

11:30 AM - 11:45 AM

[STT37-04] Bayesian Dynamic Mode Decomposition

★Invited Papers

*Hideitsu Hino1 (1.The Institute of Statistical Mathematics)

Keywords:Bayesian modeling, mode decomposition

Dynamic mode decomposition (DMD) is a data-driven deterministic method that has substantially contributed to our understanding of dynamical systems. In this work, a Bayesian formulation of DMD is proposed. It first determines the subspace of observables, and then compute the modes on that subspace. Variational matrix factorization makes it possible to realize a fully-Bayesian scheme of DMD. The proposed Bayesian DMD is capable of dealing with incomplete or missing data, which demonstrates the advantage of probabilistic modeling. Finally, both nonlinear simulated and real-world datasets are used to illustrate the potential of the proposed method. This is joint work with Mr. Takahiro Kawashima and Dr. Hayaru Shouno.