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Title: Detection of random noise and anatomy of continuous seismic waveforms in dense array data near Anza California

Abstract

SUMMARY We develop a methodology to separate continuous seismic waveforms into random noise (RN), not random noise (NRN) produced by earthquakes, wind, traffic and other sources of ground motions, and an undetermined mixture of signals. The analysis is applied to continuous records from a dense seismic array on the San Jacinto fault zone. To detect RN signals, we cut hourly waveforms into non-overlapping 1 s time windows and apply cross-correlations to separate RN candidates from outliers. The cross-correlation coefficients between different RN candidates fall into a tight range (i.e. 0.09–0.35), while cross-correlation coefficients of RN candidates with NRN signals (e.g. seismic or air-traffic events) are lower. The amplitude spectra of RN candidates have a well-defined level, while the amplitude spectra of other signals deviate from that level. Using these properties, we examine the amplitude spectra of moving time windows and cross-correlation coefficients with RN templates in each hour. The hourly RN is quasi-stationary and the results cluster tightly in the parameter space of cross-correlation coefficients and L2 norm deviations from the mean spectra of RN candidates. Time windows with parameters in this tight cluster are identified as RN, windows that deviate significantly from the RN cluster are identified as NRN andmore » windows with values in between are identified as mixed signals. Several iterations on each hourly data are used to update and stabilize the selection of RN templates and mean noise spectra. For the days examined, the relative fractions of RN, NRN and mixed signals in local day (night) times are about 26 (42 per cent), 40 (33 per cent) and 34 per cent (25 per cent), respectively.« less

Authors:
ORCiD logo [1];  [1];  [2]
  1. Department of Earth Sciences, University of Southern California, Los Angeles, CA 90089–0740, USA
  2. Scripps Institution of Oceanography, Institute of Geophysics and Planetary Physics, University of California, San Diego, La Jolla, CA 92093, USA
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1562543
Grant/Contract Number:  
SC0016520
Resource Type:
Published Article
Journal Name:
Geophysical Journal International
Additional Journal Information:
Journal Name: Geophysical Journal International Journal Volume: 219 Journal Issue: 3; Journal ID: ISSN 0956-540X
Publisher:
Oxford University Press
Country of Publication:
United Kingdom
Language:
English

Citation Formats

Meng, Haoran, Ben-Zion, Yehuda, and Johnson, Christopher W. Detection of random noise and anatomy of continuous seismic waveforms in dense array data near Anza California. United Kingdom: N. p., 2019. Web. doi:10.1093/gji/ggz349.
Meng, Haoran, Ben-Zion, Yehuda, & Johnson, Christopher W. Detection of random noise and anatomy of continuous seismic waveforms in dense array data near Anza California. United Kingdom. doi:10.1093/gji/ggz349.
Meng, Haoran, Ben-Zion, Yehuda, and Johnson, Christopher W. Sat . "Detection of random noise and anatomy of continuous seismic waveforms in dense array data near Anza California". United Kingdom. doi:10.1093/gji/ggz349.
@article{osti_1562543,
title = {Detection of random noise and anatomy of continuous seismic waveforms in dense array data near Anza California},
author = {Meng, Haoran and Ben-Zion, Yehuda and Johnson, Christopher W.},
abstractNote = {SUMMARY We develop a methodology to separate continuous seismic waveforms into random noise (RN), not random noise (NRN) produced by earthquakes, wind, traffic and other sources of ground motions, and an undetermined mixture of signals. The analysis is applied to continuous records from a dense seismic array on the San Jacinto fault zone. To detect RN signals, we cut hourly waveforms into non-overlapping 1 s time windows and apply cross-correlations to separate RN candidates from outliers. The cross-correlation coefficients between different RN candidates fall into a tight range (i.e. 0.09–0.35), while cross-correlation coefficients of RN candidates with NRN signals (e.g. seismic or air-traffic events) are lower. The amplitude spectra of RN candidates have a well-defined level, while the amplitude spectra of other signals deviate from that level. Using these properties, we examine the amplitude spectra of moving time windows and cross-correlation coefficients with RN templates in each hour. The hourly RN is quasi-stationary and the results cluster tightly in the parameter space of cross-correlation coefficients and L2 norm deviations from the mean spectra of RN candidates. Time windows with parameters in this tight cluster are identified as RN, windows that deviate significantly from the RN cluster are identified as NRN and windows with values in between are identified as mixed signals. Several iterations on each hourly data are used to update and stabilize the selection of RN templates and mean noise spectra. For the days examined, the relative fractions of RN, NRN and mixed signals in local day (night) times are about 26 (42 per cent), 40 (33 per cent) and 34 per cent (25 per cent), respectively.},
doi = {10.1093/gji/ggz349},
journal = {Geophysical Journal International},
number = 3,
volume = 219,
place = {United Kingdom},
year = {2019},
month = {7}
}

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