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DOI | 10.1029/2019GL084578 |
Machine Learning Approach to Characterize the Postseismic Deformation of the 2011 Tohoku-Oki Earthquake Based on Recurrent Neural Network | |
Yamaga, Norifumi; Mitsui, Yuta | |
2019-11-11 | |
发表期刊 | GEOPHYSICAL RESEARCH LETTERS |
ISSN | 0094-8276 |
EISSN | 1944-8007 |
出版年 | 2019 |
卷号 | 46期号:21页码:11886-11892 |
文章类型 | Article |
语种 | 英语 |
国家 | Japan |
英文摘要 | Postseismic deformation following large earthquakes has generally been analyzed via viscoelastic simulations or regression analyses that employ logarithmic and/or exponential functions. Here we introduce a machine learning approach, the recurrent neural network, to more accurately forecast postseismic deformation and constrain its characteristics. We use Global Navigation Satellite System time-series data (horizontal components) from northeastern Japan since the 2011 Tohoku-oki megathrust earthquake to assess the feasibility of this machine-learning approach. We perform numerical experiment to examine the accuracy of the neural network forecast, compare the results with those from regression analyses, and confirm the improved accuracy of the neural network forecast. The spatiotemporal evolution of the differences between the observation data and forecast results implies alterations in the source of postseismic deformation, which may have occurred in 2013. We can extract detailed information on the spatiotemporal evolution of postseismic signals by implementing this new machine-learning approach. |
英文关键词 | Machine learning Recurrent neural network GNSS 2011 Tohoku-oki earthquake Postseismic deformation Regression analysis |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000495507000001 |
WOS关键词 | SHORT-TERM ; CYCLES |
WOS类目 | Geosciences, Multidisciplinary |
WOS研究方向 | Geology |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/224986 |
专题 | 环境与发展全球科技态势 |
作者单位 | Shizuoka Univ, Dept Geosci, Shizuoka, Japan |
推荐引用方式 GB/T 7714 | Yamaga, Norifumi,Mitsui, Yuta. Machine Learning Approach to Characterize the Postseismic Deformation of the 2011 Tohoku-Oki Earthquake Based on Recurrent Neural Network[J]. GEOPHYSICAL RESEARCH LETTERS,2019,46(21):11886-11892. |
APA | Yamaga, Norifumi,&Mitsui, Yuta.(2019).Machine Learning Approach to Characterize the Postseismic Deformation of the 2011 Tohoku-Oki Earthquake Based on Recurrent Neural Network.GEOPHYSICAL RESEARCH LETTERS,46(21),11886-11892. |
MLA | Yamaga, Norifumi,et al."Machine Learning Approach to Characterize the Postseismic Deformation of the 2011 Tohoku-Oki Earthquake Based on Recurrent Neural Network".GEOPHYSICAL RESEARCH LETTERS 46.21(2019):11886-11892. |
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