Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2020GL087579 |
Detecting Slow Slip Events From Seafloor Pressure Data Using Machine Learning | |
He, Bing; Wei, Meng; Watts, D. Randolph; Shen, Yang | |
2020-05-10 | |
发表期刊 | GEOPHYSICAL RESEARCH LETTERS |
ISSN | 0094-8276 |
EISSN | 1944-8007 |
出版年 | 2020 |
卷号 | 47期号:11 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | Detecting slow slip events (SSEs) at offshore subduction zones is important to understand the slip behavior on offshore subduction megathrusts, where tsunamis can be generated. The most widely used method to detect SSEs is to measure the vertical seafloor deformation caused by SSEs using seafloor pressure data. However, due to the small signal-to-noise ratio and instrumental drift, such detection is very difficult. In this study, we trained a machine learning model using synthetic data to detect SSEs and applied it to real pressure data in New Zealand between 2014 and 2015. Our method detected five events, two of which are confirmed by the onshore GPS records. Besides, our model performs better than the traditional matched filter method. We conclude that machine learning could be used to detect SSEs in real seafloor pressure data. The method can be applied to other regions, especially where near trench GPS is not available. |
英文关键词 | slow slip events seafloor geodesy machine learning seafloor pressure data New Zealand |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000543387400009 |
WOS关键词 | SUBDUCTION ZONE ; EARTHQUAKES ; NETWORK |
WOS类目 | Geosciences, Multidisciplinary |
WOS研究方向 | Geology |
URL | 查看原文 |
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/249120 |
专题 | 气候变化 |
作者单位 | Univ Rhode Isl, Grad Sch Oceanog, Kingston, RI 02881 USA |
推荐引用方式 GB/T 7714 | He, Bing,Wei, Meng,Watts, D. Randolph,et al. Detecting Slow Slip Events From Seafloor Pressure Data Using Machine Learning[J]. GEOPHYSICAL RESEARCH LETTERS,2020,47(11). |
APA | He, Bing,Wei, Meng,Watts, D. Randolph,&Shen, Yang.(2020).Detecting Slow Slip Events From Seafloor Pressure Data Using Machine Learning.GEOPHYSICAL RESEARCH LETTERS,47(11). |
MLA | He, Bing,et al."Detecting Slow Slip Events From Seafloor Pressure Data Using Machine Learning".GEOPHYSICAL RESEARCH LETTERS 47.11(2020). |
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