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  1. 防災科研関係論文

Seismic Hazard Visualization from Big Simulation Data: Construction of a Parallel Distributed Processing System for Ground Motion Simulation Data

https://nied-repo.bosai.go.jp/records/5436
https://nied-repo.bosai.go.jp/records/5436
634ead13-c67b-4369-8719-fa44d0e6969a
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: Construction of a Parallel Distributed Processing System for 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

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抄録
内容記述タイプ Other
内容記述 We have developed a data mining system of parallel distributed processing system which is applicable to the large-scale and high-resolution numerical simulation of ground motion by transforming into ground motion indices and their statistical values, and then visualize their values for the seismic hazard information. In this system, seismic waveforms at many locations calculated for many possible earthquake scenarios can be used as input data. The system utilizes Hadoop and it calculates the ground motion indices, such as PGV, and statistical values, such as maximum, minimum, average, and standard deviation of PGV, by parallel distributed processing with MapReduce. The computation results are being an output as GIS (Geographic Information System) data file for visualization. And this GIS data is made available via the Web Map Service (WMS). In this study, we perform two benchmark tests by applying three-component synthetic waveforms at about 80,000 locations for 10 possible scenarios of a great earthquake in Nankai Trough to our system. One is the test for PGV calculation processing. Another one is the test for PGV data mining processing. A maximum of 10 parallel processing are tested for both cases. We find that our system can hold the performance even when the total tasks is larger than 10. This system can enable us to effectively study and widely distribute to the communities for disaster mitigation since it is built with data mining and visualization for hazard information by handling a large number of data from a large-scale numerical simulation.
言語 en
書誌情報 en : JOURNAL OF DISASTER RESEARCH

巻 11, 号 2, p. 265-271
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.2016.p0265
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