IDENTIFICATION OF SEISMIC SPECTRAL RATIOS BY THE DEEP LEARNING
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- TOBITA Tetsuo
- 関西大学 環境都市工学部都市システム工学科
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- YAMAMOTO Wataru
- 応用地質株式会社 流域・砂防事業部防災技術部
Bibliographic Information
- Other Title
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- 深層学習を用いた微動スペクトルの分類手法の提案
Abstract
<p> We propose a method for efficiently identifying observation points through ground motion’s spectral ratios using deep learning with convolutional neural networks (CNN). In this method, the observed acceleration spectrum is converted into a color spectrum associated with the amplitude. First, random waves were applied to multiple one-dimensional numerical model grounds, and the applicability of this method was examined using the acceleration Fourier spectrum of the ground surface response converted into a color spectrum. As a result, the model grounds were identified with an accuracy of 99% or more. Next, when using the H/V spectral ratios of seismic ground motions of less than 50 gal obtained at eight K-NET stations, the stations could be identified with an accuracy of 95% or more. The misclassified seismic motions had some characteristics such as long hypocentral distances. Furthermore, when the microtremor H/V spectrum ratio was input to the trained CNN, the accuracy was as low as about 50% on average with large dispersion.</p>
Journal
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- Japanese Journal of JSCE
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Japanese Journal of JSCE 79 (13), n/a-, 2023
Japan Society of Civil Engineers
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Details 詳細情報について
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- CRID
- 1390579057082434816
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- ISSN
- 24366021
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- Text Lang
- ja
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- Data Source
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- JaLC
- Crossref
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- Abstract License Flag
- Disallowed