气候变化领域集成服务门户(CCPortal): Predicting Imminence of Analog Megathrust Earthquakes With Machine Learning: Implications for Monitoring Subduction Zones

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DOI	10.1029/2019GL086615
	Predicting Imminence of Analog Megathrust Earthquakes With Machine Learning: Implications for Monitoring Subduction Zones
	Corbi F.; Bedford J.; Sandri L.; Funiciello F.; Gualandi A.; Rosenau M.
发表日期	2020
ISSN	0094-8276
卷号	47 期号:7
英文摘要	Subduction zones are monitored using space geodesy with increasing resolution, with the aim of better capturing the deformation accompanying the seismic cycle. Here, we investigate data characteristics that maximize the performance of a machine learning binary classifier predicting slip-event imminence. We overcome the scarcity of recorded instances from real subduction zones using data from a seismotectonic analog model monitored with a spatially dense, continuously recording onshore geodetic network. We show that a 70–85 km-wide coastal swath recording interseismic deformation gives the most important information on slip imminence. Prediction performances are mainly influenced by the alarm duration (amount of time that we consider an event as imminent), with density of stations and record length playing a secondary role. The techniques developed in this study are most likely applicable in regions of slow earthquakes, where stick-slip-like failures occur at time intervals of months to years. ©2020. The Authors.
英文关键词	Deformation; Earthquakes; Forecasting; Geodesy; Slip forming; Stick-slip; Binary classifiers; Data characteristics; Geodetic networks; Interseismic deformations; Megathrust earthquakes; Prediction performance; Seismotectonics; Subduction zones; Machine learning
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/170474
作者单位	Department of Earth Sciences, Institute of Geological Sciences, Freie Universität Berlin, Berlin, Germany; Dip. Scienze, Laboratory of Experimental Tectonics, Università “Roma TRE”, Rome, Italy; Helmholtz Centre Potsdam - GFZ German Research Centre for Geosciences, Potsdam, Germany; Istituto Nazionale di Geofisica e Vulcanologia, Bologna, Italy; Department of Geology and Planetary Sciences, California Institute of Technology, Pasadena, CA, United States
推荐引用方式 GB/T 7714	Corbi F.,Bedford J.,Sandri L.,et al. Predicting Imminence of Analog Megathrust Earthquakes With Machine Learning: Implications for Monitoring Subduction Zones[J],2020,47(7).
APA	Corbi F.,Bedford J.,Sandri L.,Funiciello F.,Gualandi A.,&Rosenau M..(2020).Predicting Imminence of Analog Megathrust Earthquakes With Machine Learning: Implications for Monitoring Subduction Zones.Geophysical Research Letters,47(7).
MLA	Corbi F.,et al."Predicting Imminence of Analog Megathrust Earthquakes With Machine Learning: Implications for Monitoring Subduction Zones".Geophysical Research Letters 47.7(2020).