Reconstruction of Rayleigh-Lamb dispersion spectrum based on noise obtained from an air-jet forcing

Abstract

The time-domain cross-correlation of incoherent and random noise recorded by a series of passive sensors contains the impulse response of the medium between these sensors. By using noise generated by a can of compressed air sprayed on the surface of a plexiglass plate, we are able to reconstruct not only the time of flight but the whole waveforms between the sensors. From the reconstruction of the direct A 0 and S 0 waves, we derive the dispersion curves of the flexural waves, thus estimating the mechanical properties of the material without a conventional electromechanical source. The dense array of receivers employed here allow a precise frequency-wavenumber study of flexural waves, along with a thorough evaluation of the rate of convergence of the correlation with respect to the record length, the frequency, and the distance between the receivers. The reconstruction of the actual amplitude and attenuation of the impulse response is also addressed in this paper.(accepted for publication in J. Acoust. Soc. Am. 2007)

In situ observations of velocity changes in response to tidal deformation from analysis of the high‐frequency ambient wavefield

“…C functions are obtained by correlating data from segments with a minimum duration of 30 min. The 0–2 correlations per day associated with a specific deformation state are then stacked over d days using weights proportional to the square root of the time window duration [ Sabra et al , ; Larose et al , ]. For each station and component of ground motion, this yields two C 1,2 functions associated with positive and negative strain for the threshold method and eight corresponding C 1,…,8 functions for the segment method.…”

Seismic Velocity Change Patterns Along the San Jacinto Fault Zone Following the 2010 M7.2 El Mayor‐Cucapah and M5.4 Collins Valley Earthquakes

“…We can expect that the scattered or diffusive propagation regime coexists with a ballistic component (Hillers et al, ; Larose et al, ). This is likely more pronounced at the longer wavelengths due to longer scattering mean free paths and the proximity of the study area to the pelagic and coastal excitation region (Gerstoft & Tanimoto, ; Hillers et al, ), in contrast to the better wavefield mixing and the more distributed natural and anthropogenic noise sources at higher frequencies (Hillers & Ben‐Zion, ; Inbal et al, ).…”

Seasonal variations of seismic velocities in the San Jacinto fault area observed with ambient seismic noise

“…We study frequency dependent fluctuations of low-noise amplitudes (Fig. 5) as a proxy for potentially biasing variations (Hadziioannou et al 2011;Zhan et al 2013), and assess systematic changes in wave propagation directions from seasonal patterns of the noise intensity B (Larose et al 2007;Froment et al 2010;Colombi et al 2014).…”

“…Scattering randomizes propagation directions and increases isotropy towards later coda arrival times. We follow the approach of Hillers et al (2013) and estimate B(θ ), where θ denotes azimuth, from correlation SNR as parametrized by Larose et al (2007). We compute intensity distributions obtained from correlation stacks associated with summer (June-August) and winter (December-February) months.…”