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

TitleDetection of random noise and anatomy of continuous seismic waveforms in dense array data near Anza California
Publication TypeJournal Article
Year of Publication2019
AuthorsMeng H.R, Ben-Zion Y., Johnson C.W
Volume219
Pagination1463-1473
Date Published2019/12
Type of ArticleArticle
ISBN Number0956-540X
Accession NumberWOS:000497982800001
Keywordsfeatures; fields; Fourier analysis; Geochemistry & Geophysics; jacinto fault zone; seismic noise; Site effects; surface; Time-series analysis; volcanic tremor; wave propagation; Wave scattering and diffraction
Abstract

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.

DOI10.1093/gji/ggz349
Student Publication: 
No