气候变化领域集成服务门户(CCPortal): Directivity Modes of Earthquake Populations with Unsupervised Learning

CCPortal

DOI	10.1029/2019JB018299
	Directivity Modes of Earthquake Populations with Unsupervised Learning
	Ross Z.E.; Trugman D.T.; Azizzadenesheli K.; Anandkumar A.
发表日期	2020
ISSN	21699313
卷号	125 期号:2
英文摘要	We present a novel approach for resolving modes of rupture directivity in large populations of earthquakes. A seismic spectral decomposition technique is used to first produce relative measurements of radiated energy for earthquakes in a spatially compact cluster. The azimuthal distribution of energy for each earthquake is then assumed to result from one of several distinct modes of rupture propagation. Rather than fitting a kinematic rupture model to determine the most likely mode of rupture propagation, we instead treat the modes as latent variables and learn them with a Gaussian mixture model. The mixture model simultaneously determines the number of events that best identify with each mode. The technique is demonstrated on four datasets in California, each with compact clusters of several thousand earthquakes with comparable slip mechanisms. We show that the datasets naturally decompose into distinct rupture propagation modes that correspond to different rupture directions, and the fault plane is unambiguously identified for all cases. We find that these small earthquakes exhibit unilateral ruptures 63–73% of the time on average. The results provide important observational constraints on the physics of earthquakes and faults. ©2020. American Geophysical Union. All Rights Reserved.
英文关键词	earthquake source properties; machine learning; rupture directivity; unsupervised learning
语种	英语
来源期刊	Journal of Geophysical Research: Solid Earth
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/187981
作者单位	Seismological Laboratory, California Institute of Technology, Pasadena, CA, United States; Geophysics Group, Los Alamos National Laboratory, Los AlamosNM, United States; Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, United States
推荐引用方式 GB/T 7714	Ross Z.E.,Trugman D.T.,Azizzadenesheli K.,et al. Directivity Modes of Earthquake Populations with Unsupervised Learning[J],2020,125(2).
APA	Ross Z.E.,Trugman D.T.,Azizzadenesheli K.,&Anandkumar A..(2020).Directivity Modes of Earthquake Populations with Unsupervised Learning.Journal of Geophysical Research: Solid Earth,125(2).
MLA	Ross Z.E.,et al."Directivity Modes of Earthquake Populations with Unsupervised Learning".Journal of Geophysical Research: Solid Earth 125.2(2020).