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  1. 防災科研関係論文

Seismic Hazard Visualization from Big Simulation Data: Cluster Analysis of Long-Period Ground-Motion Simulation Data

https://nied-repo.bosai.go.jp/records/5439
https://nied-repo.bosai.go.jp/records/5439
7c0d47c4-e8cd-4174-b26f-91f8b8736b6d
Item type researchmap(1)
公開日 2023-03-30
タイトル
言語 en
タイトル Seismic Hazard Visualization from Big Simulation Data: Cluster Analysis of Long-Period Ground-Motion Simulation Data
言語
言語 eng
著者 Takahiro Maeda

× Takahiro Maeda

en Takahiro Maeda

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Hiroyuki Fujiwara

× Hiroyuki Fujiwara

en Hiroyuki Fujiwara

Search repository
抄録
内容記述タイプ Other
内容記述 This paper describes a method of extracting the relation between the ground-motion characteristics of each area and a seismic source model, based on ground-motion simulation data output in planar form for many earthquake scenarios, and the construction of a parallel distributed processing system where this method is implemented. The extraction is realized using two-stage clustering. In the first stage, the ground-motion indices and scenario parameters are used as input data to cluster the earthquake scenarios within each evaluation mesh. In the second stage, the meshes are clustered based on the similarity of earthquake-scenario clustering. Because the mesh clusters can be correlated to the geographical space, it is possible to extract the relation between the ground-motion characteristics of each area and the scenario parameters by examining the relation between the mesh clusters and scenario clusters obtained by the two-stage clustering. The results are displayed visually; they are saved as GeoTIFF image files. The system was applied to the long-period ground-motion simulation data for hypothetical megathrust earthquakes in the Nankai Trough. This confirmed that the relation between the extracted ground-motion characteristics of each area and scenario parameters is in agreement with the results of ground-motion simulations.
言語 en
書誌情報 en : JOURNAL OF DISASTER RESEARCH

巻 12, 号 2, p. 233-240
出版者
言語 en
出版者 FUJI TECHNOLOGY PRESS LTD
ISSN
収録物識別子タイプ EISSN
収録物識別子 1883-8030
DOI
関連識別子 10.20965/jdr.2017.p0233
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