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  1. 防災科研関係論文

Ground-Motion Prediction Model Based on Neural Networks to Extract Site Properties from Observational Records

https://nied-repo.bosai.go.jp/records/3391
https://nied-repo.bosai.go.jp/records/3391
d3d9192c-f86f-4ed5-83cf-dc528fe5fb1b
Item type researchmap(1)
公開日 2023-03-30
タイトル
言語 en
タイトル Ground-Motion Prediction Model Based on Neural Networks to Extract Site Properties from Observational Records
著者 Tomohisa Okazaki

× Tomohisa Okazaki

en Tomohisa Okazaki

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Nobuyuki Morikawa

× Nobuyuki Morikawa

en Nobuyuki Morikawa

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Asako Iwaki

× Asako Iwaki

en Asako Iwaki

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Hiroyuki Fujiwara

× Hiroyuki Fujiwara

en Hiroyuki Fujiwara

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Tomoharu Iwata

× Tomoharu Iwata

en Tomoharu Iwata

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Naonori Ueda

× Naonori Ueda

en Naonori Ueda

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抄録
内容記述タイプ Other
内容記述 ABSTRACT

Choosing the method for inputting site conditions is critical in reducing the uncertainty of empirical ground-motion models (GMMs). We apply a neural network (NN) to construct a GMM of peak ground acceleration that extracts site properties from ground-motion data instead of referring to ground condition variables given for each site. A key structure of the model is one-hot representations of the site ID, that is, specifying the collection site of each ground-motion record by preparing input variables corresponding to all observation sites. This representation makes the best use of the flexibility of NN to obtain site-specific properties while avoiding overfitting at sites where a small number of strong motions have been recorded. The proposed model exhibits accurate and robust estimations among several compared models in different aspects, including data-poor sites and strong motions from large earthquakes. This model is expected to derive a single-station sigma that evaluates the residual uncertainty under the specification of estimation sites. The proposed NN structure of one-hot representations would serve as a standard ingredient for constructing site-specific GMMs in general regions.
言語 en
書誌情報 en : Bulletin of the Seismological Society of America

発行日 2021-08-01
出版者
言語 en
出版者 Seismological Society of America ({SSA})
ISSN
収録物識別子タイプ EISSN
収録物識別子 1943-3573
DOI
関連識別子 10.1785/0120200339
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