日本地震学会2022年度秋季大会

講演情報

ポスター会場(3日目)

特別セッション » S23. ベイズ統計学による地震データ解析とモデリングの深化

[S23P] PM-P

2022年10月26日(水) 13:30 〜 16:00 P-3会場 (10階(1010〜1070会議室))

13:30 〜 16:00

[S23P-02] シンプレクティックアジョイント法に基づくスロースリップ断層面の摩擦不均一性の不確実性評価

*伊藤 伸一1、加納 将行2、長尾 大道1 (1. 東京大学地震研究所、2. 東北大学)

Slip motion along a fault largely depends on the inhomogeneity of friction that occurs between the fault interfaces. Thus, it is a crucial task to estimate the spatial-dependent frictional features from the observations of the slip motion and then to identify essential parts that contribute to the principal slip motion by quantifying uncertainties involved in the estimates. This study considers an uncertainty quantification problem of the spatially-dependent frictional features based on a fault motion model that mimics the slow-slipping region along the Bungo Channel in the southwestern part of Japan. The fault motion model employs a rate-and-state dependent friction law, in which the frictional parameters are spatially dependent. Although uncertainty quantification in high-resolution is needed to attain the above task, such quantification based on the conventional statistical ways is computationally hard since the complexity exponentially increases with the spatial resolution. This study employs a variational data assimilation method based on a second-order adjoint method to avoid such complexity. Since the data assimilation method enables a selective extraction of the uncertainty of interest, we can attain a fast uncertainty quantification of the frictional parameters in high-resolution. The application of the data assimilation to the fault motion model together with the synthetic observational data of the slip velocity quantifies the spatial dependency of the uncertainty involved in the frictional parameters in high-resolution and reveals how the amount or quality of the observational data of the slip motion influences to the spatial distribution of the frictional parameters. Such quantification provides valuable information to the observational design.