TL;DR: A correlation-type reciprocity theorem is used to show that the elastodynamic Green's function of any inhomogeneous medium (random or deterministic) can be retrieved from the cross correlation of two recordings of a wave field at different receiver locations at the free surface.
Abstract: A correlation-type reciprocity theorem is used to show that the elastodynamic Green's function of any inhomogeneous medium (random or deterministic) can be retrieved from the cross correlation of two recordings of a wave field at different receiver locations at the free surface. Unlike in other derivations, which apply to diffuse wave fields in random media or irregular finite bodies, no assumptions are made about the diffusivity of the wave field. In a second version, it is assumed that the wave field is diffuse due to many uncorrelated sources inside the medium.
A correlation-type reciprocity theorem is used to show that the elastodynamic Green’s function of any inhomogeneous medium (random or deterministic) can be retrieved from the cross correlation of two recordings of a wave field at different receiver locations at the free surface.
Unlike in other derivations, which apply to diffuse wave fields in random media or irregular finite bodies, no assumptions are made about the diffusivity of the wave field.
In a second version, it is assumed that the wave field is diffuse due to many uncorrelated sources inside the medium.
TL;DR: 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.
Abstract: SUMMARY Ambient 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 repeatability 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.
1,798 citations
Cites background from "Retrieving the elastodynamic Green'..."
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Theoretical studies have shown that the cross-correlation of diffuse wavefields (e.g. ambient noise, scattered coda waves) can provide an estimate of the Green function between the stations (e.g. Weaver & Lobkis 2001a,b, 2004; Derode et al. 2003; Snieder 2004; Wapenaar 2004; Larose et al. 2005)....
TL;DR: In this article, it was shown that the acoustic Green's function between any two points in the medium can be represented by an integral of crosscorrelations of wavefield observations at those two points.
Abstract: The term seismic interferometry refers to the principle of generating new seismic responses by crosscorrelating seismic observations at different receiver locations. The first version of this principle was derived by Claerbout (1968), who showed that the reflection response of a horizontally layered medium can be synthesized from the autocorrelation of its transmission response. For an arbitrary 3D inhomogeneous lossless medium it follows from Rayleigh's reciprocity theorem and the principle of time-reversal invariance that the acoustic Green's function between any two points in the medium can be represented by an integral of crosscorrelations of wavefield observations at those two points. The integral is along sources on an arbitrarily shaped surface enclosing these points. No assumptions are made with respect to the diffusivity of the wavefield. The Rayleigh-Betti reciprocity theorem leads to a similar representation of the elastodynamic Green's function. When a part of the enclosing surface is the earth's free surface, the integral needs only to be evaluated over the remaining part of the closed surface. In practice, not all sources are equally important: The main contributions to the reconstructed Green's function come from sources at stationary points. When the sources emit transient signals, a shaping filter can be applied to correct for the differences in source wavelets. When the sources are uncorrelated noise sources, the representation simplifies to a direct crosscorrelation of wavefield observations at two points, similar as in methods that retrieve Green's functions from diffuse wavefields in disordered media or in finite media with an irregular bounding surface.
TL;DR: In this article, passive image interferometry is used to continuously monitor small temporal changes of seismic velocities in the subsurface of Mt. Merapi volcano, which is independent of sources in the classical sense and requires just one or two permanent seismic stations.
Abstract: [1] We propose passive image interferometry as a technique for seismology that allows to continuously monitor small temporal changes of seismic velocities in the subsurface. The technique is independent of sources in the classical sense and requires just one or two permanent seismic stations. We retrieve the Green’s functions that we use for interferometry from ambient seismic noise. Applying passive image interferometry to data from Merapi volcano we show that velocity variations can be measured with an accuracy of 0.1% with a temporal resolution of a single day. At Mt. Merapi the velocity variations show a strong seasonal influence and we present a depth dependent hydrological model that describes our observations solely based on precipitation. Citation: Sens
610 citations
Cites background from "Retrieving the elastodynamic Green'..."
...Green’s function (GF, or impulse response) between two points A and B can be retrieved by cross-correlation of a random isotropic wave field sensed at A and B [Derode et al., 2003; Sánchez-Sesma and Campillo, 2006; Wapenaar, 2004; Snieder, 2004]....
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...The basic principle of passive imaging is that the Green’s function (GF, or impulse response) between two points A and B can be retrieved by cross-correlation of a random isotropic wave field sensed at A and B [Derode et al., 2003; Sánchez-Sesma and Campillo, 2006; Wapenaar, 2004; Snieder, 2004]....
TL;DR: In this article, a cross-correlations of long time-series of ambient noise data is computed in daily segments, stacked over 1 yr, and Rayleigh wave group dispersion curves from 8 to 50 s period are measured using a phase-matched filter, frequency time analysis technique.
Abstract: SUMMARY We extend ambient noise surface wave tomography both in bandwidth (10‐50 s period) and in geographical extent (across much of Europe) compared with previous applications. 12 months of ambient noise data from 2004 are analysed. The data are recorded at about 125 broadband Seismic stations from the Global Seismic Network and the Orfeus Virtual European Broad-band seismic Network. Cross-correlations are computed in daily segments, stacked over 1 yr, and Rayleigh wave group dispersion curves from 8 to 50 s period are measured using a phase-matched filter, frequency time analysis technique. We estimate measurement uncertainties using the seasonal variation of the dispersion curves revealed in 3 month stacks. On average, uncertainties in group delays increase with period from ∼ 3t o∼7 s from periods of 10 to 50 s, respectively. Group speed maps at periods from 10 to 50 s are estimated. The resulting path coverage is denser and displays a more uniform azimuthal distribution than from earthquake-emitted surface waves. The fit of the group speed maps to the ambient noise data is significantly improved below 30 s compared to the fit achieved with earthquake data. Average resolution is estimated to be about 100 km at 10 s period, but degrades with increasing period and toward the periphery of the study region. The resulting ambient noise group speed maps demonstrate significant agreement with known geological and tectonic features. In particular, the signatures of sedimentary basins and crustal thickness are revealed clearly in the maps. These results are evidence that surface wave tomography based on crosscorrelations of long time-series of ambient noise data can be achieved over a broad period band on nearly a continental scale and yield higher resolution and more reliable group speed maps than based on traditional earthquake-based measurements.
533 citations
Cites background from "Retrieving the elastodynamic Green'..."
...Theoretical research has shown that, under the right circumstances, the cross-correlation of records from two seismic stations provides an estimate of the Green function between the stations, modulated by the spectrum of the noise source (Weaver and Lobkis, 2001a, 2001b, 2004; Derode et al., 2003; Snieder, 2004; Wapenaar, 2004; Larose et al., 2005)....
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...…the right circumstances, the cross-correlation of records from two seismic stations provides an estimate of the Green’s function between the stations, modulated by the spectrum of the noise source (Weaver & Lobkis 2001a,b, 2004; Derode et al. 2003; Snieder 2004; Wapenaar 2004; Larose et al. 2005)....
TL;DR: Sabra et al. as mentioned in this paper used a simple but densely sampled tomographic procedure to estimate the surface wave velocity structure within the frequency range of 0.1-0.2 Hz for a region in Southern California.
Abstract: Received 5 April 2005; revised 23 May 2005; accepted 9 June 2005; published 26 July 2005. [1] Since it has already been demonstrated that point-topoint seismic propagation Green Functions can be extracted from seismic noise, it should be possible to image Earth structure using the ambient noise field. Seismic noise data from 148 broadband seismic stations in Southern California were used to extract the surface wave arrival-times between all station pairs in the network. The seismic data were then used in a simple, but densely sampled tomographic procedure to estimate the surface wave velocity structure within the frequency range of 0.1–0.2 Hz for a region in Southern California. The result compares favorably with previous estimates obtained using more conventional and elaborate inversion procedures. This demonstrates that coherent noise field between station pairs can be used for seismic imaging purposes. Citation: Sabra, K. G., P. Gerstoft, P. Roux, W. A. Kuperman, and M. C. Fehler (2005), Surface wave tomography from microseisms in Southern California, Geophys. Res. Lett., 32, L14311, doi:10.1029/2005GL023155.
Q1. What are the contributions in "Retrieving the elastodynamic green’s function of an arbitrary inhomogeneous medium by cross correlation" ?
In this paper, a correlation-type reciprocity theorem is used to show that the elastodynamic Green 's function of any inhomogeneous medium ( random or deterministic ) can be retrieved from the cross correlation of two recordings of a wave field at different receiver locations at the free surface.