气候变化领域集成服务门户(CCPortal): Detecting Slow Slip Events From Seafloor Pressure Data Using Machine Learning

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DOI	10.1029/2020GL087579
	Detecting Slow Slip Events From Seafloor Pressure Data Using Machine Learning
	He B.; Wei M.; Watts D.R.; Shen Y.
发表日期	2020
ISSN	0094-8276
卷号	47 期号:11
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. ©2020. American Geophysical Union. All Rights Reserved.
英文关键词	Matched filters; Offshore oil well production; Signal to noise ratio; Machine learning models; Pressure data; Seafloor deformation; Slip behavior; Slow slip events; Subduction megathrusts; Subduction zones; Synthetic data; Machine learning; GPS; machine learning; numerical model; seafloor; signal-to-noise ratio; slip; subduction zone; thrust; New Zealand
语种	英语
来源期刊	Geophysical Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/170297
作者单位	Graduate School of Oceanography, University of Rhode Island, Kingston, RI, United States
推荐引用方式 GB/T 7714	He B.,Wei M.,Watts D.R.,et al. Detecting Slow Slip Events From Seafloor Pressure Data Using Machine Learning[J],2020,47(11).
APA	He B.,Wei M.,Watts D.R.,&Shen Y..(2020).Detecting Slow Slip Events From Seafloor Pressure Data Using Machine Learning.Geophysical Research Letters,47(11).
MLA	He B.,et al."Detecting Slow Slip Events From Seafloor Pressure Data Using Machine Learning".Geophysical Research Letters 47.11(2020).