Title: Identifying Different Classes of Seismic Noise Signals Using Unsupervised Learning

Abstract

Abstract 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.

Authors:

Johnson, Christopher W.  ^[1];

• Search DOE PAGES for author "Johnson, Christopher W."
• Search DOE PAGES for ORCID "0000-0002-2589-7593"

Ben‐Zion, Yehuda  ^[2];

• Search DOE PAGES for author "Ben‐Zion, Yehuda"
• Search DOE PAGES for ORCID "0000-0002-9602-2014"

Meng, Haoran  ^[2];

• Search DOE PAGES for author "Meng, Haoran"
• Search DOE PAGES for ORCID "0000-0002-1951-2319"

Vernon, Frank  ^[3]

• Search DOE PAGES for author "Vernon, Frank"
• Search DOE PAGES for ORCID "0000-0002-9379-4000"

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+ Show Author Affiliations

1. Scripps Institution of Oceanography University of California, San Diego San Diego CA USA, Now at Los Alamos National Laboratory Los Alamos NM USA
2. Department of Earth Sciences University of Southern California Los Angeles CA USA
3. Scripps Institution of Oceanography University of California, San Diego San Diego CA USA

Publication Date:

Mon Aug 03 00:00:00 EDT 2020

Sponsoring Org.:

USDOE

OSTI Identifier:

1644360

Grant/Contract Number:  

DE‐SC0016520; DE‐SC0016527

Resource Type:

Publisher's Accepted Manuscript

Journal Name:

Geophysical Research Letters

Additional Journal Information:

Journal Name: Geophysical Research Letters Journal Volume: 47 Journal Issue: 15; Journal ID: ISSN 0094-8276

Publisher:

American Geophysical Union (AGU)

Country of Publication:

United States

Language:

English