气候变化领域集成服务门户(CCPortal): Identifying Different Classes of Seismic Noise Signals Using Unsupervised Learning

CCPortal

DOI	10.1029/2020GL088353
	Identifying Different Classes of Seismic Noise Signals Using Unsupervised Learning
	Johnson C.W.; Ben-Zion Y.; Meng H.; Vernon F.
发表日期	2020
ISSN	0094-8276
卷号	47 期号:15
英文摘要	Proper classification of nontectonic seismic signals is critical for detecting microearthquakes and developing an improved understanding of ongoing weak ground motions. We use unsupervised machine learning to label five classes of nonstationary seismic noise common in continuous waveforms. Temporal and spectral features describing the data are clustered to identify separable types of emergent and impulsive waveforms. The trained clustering model is used to classify every 1 s of continuous seismic records from a dense seismic array with 10–30 m station spacing. We show that dominate noise signals can be highly localized and vary on length scales of hundreds of meters. The methodology demonstrates the complexity of weak ground motions and improves the standard of analyzing seismic waveforms with a low signal-to-noise ratio. Application of this technique will improve the ability to detect genuine microseismic events in noisy environments where seismic sensors record earthquake-like signals originating from nontectonic sources. © 2020. American Geophysical Union. All Rights Reserved.
英文关键词	Seismic waves; Signal to noise ratio; Clustering model; Continuous waveforms; Low signal-to-noise ratio; Micro-earthquakes; Microseismic events; Noisy environment; Seismic waveforms; Unsupervised machine learning; Earthquakes; complexity; ground motion; seismic noise; signal-to-noise ratio; spectral analysis; unsupervised classification; waveform analysis
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/170051
作者单位	Scripps Institution of Oceanography, University of California, San Diego, San Diego, CA, United States; Now at Los Alamos National Laboratory, Los Alamos, NM, United States; Department of Earth Sciences, University of Southern California, Los Angeles, CA, United States
推荐引用方式 GB/T 7714	Johnson C.W.,Ben-Zion Y.,Meng H.,et al. Identifying Different Classes of Seismic Noise Signals Using Unsupervised Learning[J],2020,47(15).
APA	Johnson C.W.,Ben-Zion Y.,Meng H.,&Vernon F..(2020).Identifying Different Classes of Seismic Noise Signals Using Unsupervised Learning.Geophysical Research Letters,47(15).
MLA	Johnson C.W.,et al."Identifying Different Classes of Seismic Noise Signals Using Unsupervised Learning".Geophysical Research Letters 47.15(2020).