[S-SS33] 都市の脆弱性が引き起こす激甚災害の軽減化プロジェクト

[SSS33-P02] レプリカ交換モンテカルロ法を用いた首都圏地震動イメージング

*加納 将行¹、長尾 大道^1,2、石川 大智²、酒井 慎一¹、中川 茂樹¹、堀 宗朗¹、平田 直¹ (1.東京大学地震研究所、2.東京大学情報理工学系研究科)

キーワード：マルコフ連鎖モンテカルロ法、MeSO-net、波動場、地震動

Earthquakes sometimes cause serious disasters not only directly by ground motion itself but also secondarily by damage of infrastructures, especially in the case of metropolitan areas that have numerous populations and capital functions in the country. For reducing such the secondary disasters, it is potent to evaluate seismic hazards rapidly by analyzing seismic response of each structure due to ground motion input from the bottom. In this study, we propose a methodology that consists of physics-based and data-driven approaches, in order to obtain seismic wave field as an input for seismic response analysis of structures. One of the Markov chain Monte Carlo (MCMC) methods, the replica exchange Monte Carlo, is adopted for the estimation of seismic wave field together with local crustal structure. Two numerical tests are conducted to examine the feasibility of the proposed method using the analytical solution with a horizontally layered crustal structure. The geometry of observation sites is referred to dense seismological network, Metropolitan seismic observation network (MeSO-net). It is confirmed that 1) the proposed method is possible to search the parameters related to the local crustal structure in broader space compared to a fundamental MCMC method, Metropolis method and 2) the seismic wave field estimated by the proposed method is almost coincident with the true wave field even if the local crustal structures are not so well estimated around the assumed values. On the other hand, the wave field estimated by the ordinary kriging, a classical interpolation method for spatial data, is hardly possible to reproduce the wave propagation and is much different from the true one even in low frequencies. This indicates that the proposed combined method taking both physics-based and data-driven approaches into consideration is essential for the seismic wave field imaging utilizing a dense observation network like MeSO-net. Acknowledgments: This research is supported by the Special project for reducing vulnerability for urban mega earthquake disasters from the Ministry of Education, Culture, Sports, Science and Technology of Japan.