Processing seismic ambient noise data to obtain reliable broad-band surface wave dispersion measurements

Abstract

International audienceAmbient noise tomography is a rapidly emerging field of seismological research. This paper presents the current status of ambient noise data processing as it has developed over the past several years and is intended to explain and justify this development through salient examples. The ambient noise data processing procedure divides into four principal phases: (1) single station data preparation, (2) cross-correlation and temporal stacking, (3) measurement of dispersion curves (performed with frequency–time analysis for both group and phase speeds) and (4) quality control, including error analysis and selection of the acceptable measurements. The procedures that are described herein have been designed not only to deliver reliable measurements , but to be flexible, applicable to a wide variety of observational settings, as well as being fully automated. For an automated data processing procedure, data quality control measures are particularly important to identify and reject bad measurements and compute quality assurance statistics for the accepted measurements. The principal metric on which to base a judgment of quality is stability, the robustness of the measurement to perturbations in the conditions under which it is obtained. Temporal repeatability, in particular, is a significant indicator of reliability and is elevated to a high position in our assessment, as we equate seasonal repeata-bility with measurement uncertainty. Proxy curves relating observed signal-to-noise ratios to average measurement uncertainties show promise to provide useful expected measurement error estimates in the absence of the long time-series needed for temporal subsetting

Two-receiver measurements of phase velocity: cross-validation of ambient-noise and earthquake-based observations

“…In this approach, the zero crossings of the real part of the cross-correlation spectrum are evaluated to derive the phase-velocity dispersion. Importantly, this means that the Green's function's amplitude does not have to be reconstructed, which is the case when phase is to be determined by analysis of time-domain traces (Bensen et al 2007;Lin et al 2008;Zhou et al 2012;Verbeke et al 2012). We verify that our algorithm can successfully measure the phase velocity of both Love and Rayleigh waves from ambient noise.…”

“…Summing cross-correlations over time is what is generally implemented in practice (e.g. Bensen et al 2007).…”

Seismic interferometry and ambient noise tomography in the British Isles

“…The next step involves applying time domain normalisation in order to remove the influence of large amplitude events such as earthquakes and other non-stationary noise sources from the subsequent cross-correlation. Bensen et al (2007) describe various methods of applying time domain normalisation, however we decided that computationally less expensive one-bit normalisation (i.e. only the sign of the signal is retained) was satisfactory for our purposes since the British Isles are more or less aseismic.…”

“…We mostly follow the data processing procedure as described in detail by Bensen et al (2007) which is summarised as follows: first, we cut the continuous noise data into files of 24 h in length, then remove each seismometer's instrumental response, and the mean and linear trends from the day-files. To reduce the amount of storage space and computational time required, the data are then decimated to one sample per second.…”

“…The use of wavefield or seismic interferometry has increased greatly in recent years and in this time has been applied in many novel ways to retrieve useful signals from background noise sources (e.g. Rickett and Claerbout, 1999;Lobkis and Weaver, 2001;Weaver and Lobkis, 2001;Campillo and Paul, 2003;Shapiro and Campillo, 2004;Sabra et al, 2005a,b;Shapiro et al, 2005;Yang et al, 2006;Draganov et al, 2006;Bensen et al, 2007Bensen et al, , 2008Zheng et al, 2008) and active or impulsive sources (e.g. Bakulin and Calvert, 2006;Slob et al, 2007;Lu et al, 2008;King et al, 2010), for computing or modelling synthetic waveforms (van-Manen et al, 2005(van-Manen et al, , 2006, and for noise prediction and removal from data Dong et al, 2006;Halliday et al, 2007Halliday et al, , 2010Curtis, 2008, 2009).…”