Lab 4. Hiromichi Nagao


Hiromichi Nagao
Hiromichi Nagao

Department of Mathematical Informatics, Graduate School of Information Science and Technology, University of Tokyo
Associate Professor

1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-0032 Eat. 3 Bldg. Room 33
Tel: +81-3-5841-1766 (ext. 21766)


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Curriculum Vitae

Mar. 1995 Bachelor degree from Faculty of Science, Kyoto University
Mar. 1997 Master degree from Graduate School of Science, Kyoto University
Mar. 2002 Ph. D., Graduate School of Science, Kyoto University
Apr. 2002 Visiting Researcher, Japan Nuclear Cycle Development Institute
Mar. 2006 Researcher, Japan Agency for Marine-Earth Science and Technology
Jun. 2009 Project Researcher, The Institute of Statistical Mathematics
Dec. 2010 Project Associate Professor, The Institute of Statistical Mathematics
Sep. 2013 Associate Professor, Earthquake Research Institute, The University of Tokyo
Oct. 2013 Associate Professor, Graduate School of Information Science and Technology, The University of Tokyo

Research Themes

We could not prevent the damage from spreading caused by the Great East Japan Earthquake, which took place on March 11, 2011, despite the recent developments of global-scale real-time observational networks and large-scale numerical simulations based on high-performance computing.
In order to save as many human lives as possible from future great earthquakes, we are dedicating to accumulate comprehensive knowledge through integration of observation and simulation data related to earthquakes, tsunamis and seismic hazards based on statistical methodologies such as data assimilation.

1. Data Assimilation
Data assimilation is a computational technique to integrate numerical simulation models and observational/experimental data based on Bayesian statistics. Data assimilation provides simulation models that are possible to predict the future, sequentially estimating parameters involved in the simulation models and state vectors at every time step. Data assimilation was originally developed in meteorology and oceanology; for example, the weather forecasting absolutely shows results of data assimilation. We develop data assimilation techniques for the solid Earth science to investigate earthquakes and tsunamis.

2. Sequential Bayesian Filters and Four-Dimensional Variational Method
In data assimilation, an appropriate method is to be selected from various types of sequential Bayesian filters or four-dimensional variational method (4DVar) to compare predictions obtained by numerical simulations and observational data, considering the purpose and computational cost. We have been developing new algorithms of sequential Bayesian filters and 4DVar that are suitable for practical problems in the solid Earth science.

Selected papers

Sasaki, K., A. Yamanaka, S. Ito, and H. Nagao, Data assimilation for phase-field models based on the ensemble Kalman filter, Computational Materials Science, Vol. 141, pp. 141-152, doi:10.1016/j.commatsci.2017.09.025, 2018.
Ito, S., H. Nagao, T. Kasuya, and J. Inoue, Grain growth prediction based on data assimilation by implementing 4DVar on multi-phase-field model, Science and Technology of Advanced Materials, Vol. 18, Issue 1, pp. 857-869, doi:10.1080/14686996.2017.1378921, 2017.
Kano, M., H. Nagao, K. Nagata, S. Ito, S. Sakai, S. Nakagawa, M. Hori, and N. Hirata, Seismic wavefield imaging of long-period ground motion in the Tokyo Metropolitan area, Japan, J. Geophys. Res. Solid Earth, Vol. 122, doi:10.1002/2017JB014276, 2017.
Kano, M., H. Nagao, D. Ishikawa, S. Ito, S. Sakai, S. Nakagawa, M. Hori, and N. Hirata, Seismic wavefield imaging based on the replica exchange Monte Carlo method, Geophys. J. Int., Vol. 208, pp. 529-545, doi:10.1093/gji/ggw410, 2017.
Ito, S., H. Nagao, A. Yamanaka, Y. Tsukada, T. Koyama, M. Kano, and J. Inoue, Data assimilation for massive autonomous systems based on a second-order adjoint method, Phys. Rev. E, 94, 043307, doi:10.1103/PhysRevE.94.043307, 2016.