[1G4-OS-22a] シミュレーションとAI(1/2)

[1G4-OS-22a-01] Deepening of Earthquake Research Based on Data Science

〇Hiromichi Nagao^1,2, Shin-ichi Ito^1,2, Ryosuke Kaneko² (1. Earthquake Research Institute, The University of Tokyo, 2. Graduate School of Information Science and Technology, The University of Tokyo)

Keywords:data assimilation, deep learning, seismology, adjoint method, ResNet

Data science techniques are indispensable in seismology, which bases large-scale numerical simulations and big data analyses. A new national project "Seismology TowArd Research innovation with data of Earthquake" (STAR-E) launched in 2021 by Ministry of Education, Culture, Sports, Science and Technology, Japan, is accelerating the integration of information science and seismology. In this paper, we introduce two examples of data science techniques applied in seismology, (1) data assimilation to integrate numerical simulations and observational data based on Bayesian statistics, and (2) deep learning to detect evidence of ordinary earthquakes and slow earthquakes from seismic observational data. We also discuss the importance of integrating data assimilation and artificial intelligence in future to reduce the computational cost required in numerical simulations of seismological issues such as seismic wave propagation.