Performance of different processing schemes in seismic noise cross-correlations

Abstract

S U M M A R YThe estimation of the Green's function between two points on the Earth's surface by the crosscorrelation of seismic noise time-series became a widely used method in seismology. In general, very long time-series (months to years) as well as massive normalization and/or data selection are necessary to obtain useful cross-correlation functions. One task of this study is to evaluate the influence of different established normalization methods on the obtained cross-correlation functions. Furthermore, we evaluate two waveform preserving time domain normalizations as well as a new fully automated data selection approach. The cross-correlation functions analysed in this study are obtained from 12 months of seismic noise recorded in 2004 at five seismic stations in the United States with station distances on a continental scale. For practical reasons, the cross-correlation functions of such long time-series are calculated by stacking the cross-correlation functions obtained from shorter time windows. We use this stacking process for the implementation of the waveform preserving time domain normalizations. The time window length is in general an important parameter of the cross-correlation processing, as it influences the normalization and data selection. Therefore, we evaluate the cross-correlation functions obtained with 47 different time window lengths between one hr and 24 hr. The time domain normalizations intend to suppress the influence of transient signals like earthquake waves as well as long-term (e.g. seasonal) amplitude variations. We compare the proposed waveform preserving time domain normalizations with the established running absolute mean normalization and the one-bit normalization. We demonstrate that a waveform preserving time domain normalization can replace a non-linear time domain normalization, if a time window length similar to the duration of the typically occurring transient signals is used. Next to the time domain normalizations also the spectral whitening in the frequency domain is evaluated. Spectral whitening is a powerful normalization to improve the emergence of broadband signals in seismic noise cross-correlations. Nevertheless, we observe spectral whitening to depend strongly on the time window length. An unwanted amplification of a persistent microseism signal is observed on the continental scale with time windows shorter than 12 hr. Our approach of automated data selection is based on a statistical time-series classification and reliably excludes time windows with transient signals occurring contemporaneously at both sites (e.g. earthquake waves). This data selection approach is capable to replace a nonlinear time domain normalization, but no improvement of the waveform symmetry or the signal-to-noise ratio of the cross-correlation functions is observed in general.

Passive seismic monitoring with nonstationary noise sources

“…We deliberately do not apply non-linear processing techniques, such as spectral whitening or one-bit normalization (Bensen et al, 2007;Groos et al, 2012), even though the latter has been shown to not add a systematic bias to the measurement in the case of Gaussian wavefield statistics and for a variety of other distributions (Hanasoge and Branicki, 2013;Fichtner et al, 2016). We deliberately do not apply non-linear processing techniques, such as spectral whitening or one-bit normalization (Bensen et al, 2007;Groos et al, 2012), even though the latter has been shown to not add a systematic bias to the measurement in the case of Gaussian wavefield statistics and for a variety of other distributions (Hanasoge and Branicki, 2013;Fichtner et al, 2016).…”

“…Mulgaria, 2012;Sens-Schoenfelder et al, 2015). Bensen et al, 2007;Schimmel et al, 2011;Groos et al, 2012), but the procedures are subjective and lead to hardly predictable effects on the sensitivity of noise correlations to Earth structure (Fichtner, 2014). Peterson, 1993;Schulte-Pelkum et al, 2004;Bonnefoy-Claudet et al, 2006;Stehly et al, 2006;Tanimoto et al, 2006;Stutzmann et al, 2012;Latorre et al, 2014;Ermert et al, 2016).…”

Reflecting boundary conditions for interferometry by multidimensional deconvolution

“…9,28 Furthermore, temporal normalization and/or spectral whitening may need to be applied to mitigate the adverse effects arising from the lack of stationarity of the recorded noise field. 29,30 Inversion of Eq. (12) is similar to the inversion of Eq.…”

Phase velocity tomography of surface waves using ambient noise cross correlation and array processing

“…Studies on ambient noise data often present different approaches to preprocess these data before cross correlation. The processing employed is slightly modified from Sabra et al [] and Bensen et al [], with transient event removal using a shorter time window [ Groos et al , ]. This section presents the details of the noise data preprocessing.…”