Time domain classification and quantification of seismic noise in an urban environment

Details Time domain classification and quantification of seismic noise in an urban environment Time domain classification and quantification of seismic noise in an urban environment

Nov 2009

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009geoji.179.1213g&link_type=abstract

Geophysical Journal International, Volume 179, Issue 2, pp. 1213-1231.

Astronomy and Astrophysics

Astronomy

2

Time-Series Analysis, Site Effects, Statistical Seismology

Scientific paper

Broad-band urban seismic noise (USN) must be considered as a temporally and spatially non-stationary random process. Due to the high variability of USN a single measure like the standard deviation of a seismic noise time-series or the power spectral density at a given frequency is not enough to characterize a sample (time-series) of USN comprehensively. Therefore, we use long-term spectrograms and propose an automated statistical classification in the time domain to quantify and characterize USN. Long-term spectrograms of up to 28 d duration are calculated from a broad-band seismic data set recorded in the metropolitan area of Bucharest, Romania, to identify the frequency-dependent behaviour of the time-variable processes contributing to USN. Based on the spectral analysis eight frequency ranges between 8 mHz and 45 Hz are selected for our proposed time domain classification. The classification scheme identifies deviations from the Gaussian distribution of 4-hr-long time-series of USN. Our classification is capable to identify Gaussian distributed seismic noise time-series as well as time-series dominated by transient or periodic signals using six noise classes. Four additional noise classes are introduced to identify corrupt time-series. The performance of the method is tested with a synthetic data set. We also apply the statistical classification for the data set from Bucharest in three time windows (0-4, 8-12 and 13-17 EET) at 11 d in the eight frequency ranges. Only 40 per cent of the analysed time-series are observed to be Gaussian distributed. Most common deviations from the Gaussian distribution (~47 per cent) are due to the influence of large-amplitude transient signals. In all frequency ranges between 0.04 and 45 Hz significant variations of the statistical properties of USN are observed with daytime, indicating the broad-band human influence on USN. We observe the human activity as a dominant influence on the USN above and below the frequency band of ocean-generated microseism between 0.04 and 0.6 Hz. Our time domain classification and quantification is furthermore capable to resolve the influence of wind on seismic noise and a known site effect variation in the metropolitan area of Bucharest. The information about noise amplitudes and statistical properties derived automatically from broad-band seismic data can be used to select time windows containing adequate data for seismic noise utilization like H/V-studies or ambient noise tomography.

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