气候变化领域集成服务门户(CCPortal): Similarity of fast and slow earthquakes illuminated by machine learning

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DOI	10.1038/s41561-018-0272-8
	Similarity of fast and slow earthquakes illuminated by machine learning
	Hulbert C.; Rouet-Leduc B.; Johnson P.A.; Ren C.X.; Rivière J.; Bolton D.C.; Marone C.
发表日期	2019
ISSN	17520894
卷号	12 期号:1
英文摘要	Tectonic faults fail in a spectrum of modes, ranging from earthquakes to slow slip events. The physics of fast earthquakes are well described by stick–slip friction and elastodynamic rupture; however, slow earthquakes are poorly understood. Key questions remain about how ruptures propagate quasi-dynamically, whether they obey different scaling laws from ordinary earthquakes and whether a single fault can host multiple slip modes. We report on laboratory earthquakes and show that both slow and fast slip modes are preceded by a cascade of micro-failure events that radiate elastic energy in a manner that foretells catastrophic failure. Using machine learning, we find that acoustic emissions generated during shear of quartz fault gouge under normal stress of 1–10 MPa predict the timing and duration of laboratory earthquakes. Laboratory slow earthquakes reach peak slip velocities of the order of 1 × 10−4 m s−1 and do not radiate high-frequency elastic energy, consistent with tectonic slow slip. Acoustic signals generated in the early stages of impending fast laboratory earthquakes are systematically larger than those for slow slip events. Here, we show that a broad range of stick–slip and creep–slip modes of failure can be predicted and share common mechanisms, which suggests that catastrophic earthquake failure may be preceded by an organized, potentially forecastable, set of processes. © 2018, This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.
语种	英语
来源期刊	Nature Geoscience
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/124741
作者单位	Geophysics Group, Los Alamos National Laboratory, Los Alamos, NM, United States; Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, United States; Department of Geosciences, Pennsylvania State University, University Park, PA, United States
推荐引用方式 GB/T 7714	Hulbert C.,Rouet-Leduc B.,Johnson P.A.,et al. Similarity of fast and slow earthquakes illuminated by machine learning[J],2019,12(1).
APA	Hulbert C..,Rouet-Leduc B..,Johnson P.A..,Ren C.X..,Rivière J..,...&Marone C..(2019).Similarity of fast and slow earthquakes illuminated by machine learning.Nature Geoscience,12(1).
MLA	Hulbert C.,et al."Similarity of fast and slow earthquakes illuminated by machine learning".Nature Geoscience 12.1(2019).