S U M M A R YPhase velocities derived from ambient-noise cross-correlation are compared with phase velocities calculated from cross-correlations of waveform recordings of teleseismic earthquakes whose epicentres are approximately on the station-station great circle. The comparison is conducted both for Rayleigh and Love waves using over 1000 station pairs in central Europe. We describe in detail our signal-processing method which allows for automated processing of large amounts of data. Ambient-noise data are collected in the 5-80 s period range, whereas teleseismic data are available between about 8 and 250 s, resulting in a broad common period range between 8 and 80 s. At intermediate periods around 30 s and for shorter interstation distances, phase velocities measured from ambient noise are on average between 0.5 per cent and 1.5 per cent lower than those observed via the earthquake-based method. This discrepancy is small compared to typical phase-velocity heterogeneities (10 per cent peak-to-peak or more) observed in this period range.We nevertheless conduct a suite of synthetic tests to evaluate whether known biases in ambient-noise cross-correlation measurements could account for this discrepancy; we specifically evaluate the effects of heterogeneities in source distribution, of azimuthal anisotropy in surface-wave velocity and of the presence of near-field, rather than far-field only, sources of seismic noise. We find that these effects can be quite important comparing individual station pairs. The systematic discrepancy is presumably due to a combination of factors, related to differences in sensitivity of earthquake versus noise data to lateral heterogeneity. The data sets from both methods are used to create some preliminary tomographic maps that are characterized by velocity heterogeneities of similar amplitude and pattern, confirming the overall agreement between the two measurement methods.

We apply seismic interferometry to data from an OBS survey offshore Norway and show that ambient seismic noise can be used to constrain subsurface attenuation on a reservoir scale.By crosscorrelating only a few days of recordings by broad-band ocean bottom seismometers we are able to retrieve empirical Green's Functions (EGFs) associated with surface waves in the frequency range between 0.2 and 0.6 Hz and acoustic waves traveling through the seawater between 1.0 and 2.5 Hz. We show that the decay of theses u r f a c ew a v e sc a n n o t be explained by geometrical spreading alone and requires an additional loss of energy with distance. We quantify this observed attenuation in the frequency domain using a modified 1 Bessel function to describe the cross-spectrum in a stationary field. We average crossspectra of equally spaced station couples and sort these azimuthally averaged cross-spectra with distance. We then obtain frequency-dependent estimates of attenuation by minimizing the misfit of the real parts to a damped Bessel function. The resulting quality factors as function of frequency are indicative of the depth variation of attenuation and correlate with the geology in the survey area.2

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The cross correlation of ambient signal allows seismologists to collect data even in the absence of seismic events. "Seismic interferometry" shows that the cross correlation of simultaneous recordings of a random wavefield made at two locations is formally related to the impulse response between those locations. This idea has found many applications in seismology, as a growing number of dense seismic networks become available: cross-correlating long seismic records, the Green's function between instrument pairs is "reconstructed" and used, just like the seismic recording of an explosion, in tomography, monitoring, etc. These applications have been accompanied by theoretical investigations of the relationship between noise cross correlation and the Green's function; numerous formulations of "ambient noise" theory have emerged, each based on different hypotheses and/or analytical approaches. The purpose of this study is to present most of those approaches together, providing a comprehensive overview of the theory. Understanding the specific hypotheses behind each Green's function recipe is critical to its correct application. Hoping to guide nonspecialists who approach ambient noise theory for the first time, we treat the simplest formulation (the stationary-phase approximation applied to smooth unbounded media) in detail. We then move on to more general treatments, illustrating that the "stationary-phase" and "reciprocity theorem" approaches lead to the same formulae when applied to the same scenario. We show that a formal cross correlation/Green's function relationship can be found in complex, bounded media and for nonuniform source distributions. We finally provide the bases for understanding how the Green's function is reconstructed in the presence of scattering obstacles.

Measuring attenuation on the basis of interferometric, receiver-receiver surface waves is a non-trivial task: the amplitude, more than the phase, of ensemble-averaged cross-correlations is strongly affected by non-uniformities in the ambient wavefield. In addition, ambient noise data are typically pre-processed in ways that affect the amplitude itself. Some authors have recently attempted to measure attenuation in receiver-receiver cross-correlations obtained after the usual pre-processing of seismic ambient-noise records, including, most notably, spectral whitening. Spectral whitening replaces the cross-spectrum with a unit amplitude spectrum. It is generally assumed that cross-terms have cancelled each other prior to spectral whitening. Cross-terms are peaks in the cross-correlation due to simultaneously acting noise sources, that is, spurious traveltime delays due to constructive interference of signal coming from different sources. Cancellation of these cross-terms is a requirement for the successful retrieval of interferometric receiver-receiver signal and results from ensemble averaging. In practice, ensemble averaging is replaced by integrating over sufficiently long time or averaging over several cross-correlation windows. Contrary to the general assumption, we show in this study that cross-terms are not required to cancel each other prior to spectral whitening, but may also cancel each other after the whitening procedure. Specifically, we derive an analytic approximation for the amplitude difference associated with the reversed order of cancellation and normalization. Our approximation shows that an amplitude decrease results from the reversed order. This decrease is predominantly non-linear at small receiverreceiver distances: at distances smaller than approximately two wavelengths, whitening prior to ensemble averaging causes a significantly stronger decay of the cross-spectrum.

During the breakup of continents in magmatic settings, the extension of the rift valley is commonly assumed to initially occur by border faulting and progressively migrate in space and time toward the spreading axis. Magmatic processes near the rift flanks are commonly ignored. We present phase velocity maps of the crust and uppermost mantle of the conjugate margins of the southern Red Sea (Afar and Yemen) using ambient noise tomography to constrain crustal modification during breakup. Our images show that the low seismic velocities characterize not only the upper crust beneath the axial volcanic systems but also both upper and lower crust beneath the rift flanks where ongoing volcanism and hydrothermal activity occur at the surface. Magmatic modification of the crust beneath rift flanks likely occurs for a protracted period of time during the breakup process and may persist through to early seafloor spreading.

S U M M A R YObtaining new seismic responses from existing recordings is generally referred to as seismic interferometry (SI). Conventionally, the SI responses are retrieved by simple crosscorrelation of recordings made by separate receivers: one of the receivers acts as a 'virtual source' whose response is retrieved at the other receivers. When SI is applied to recordings of ambient seismic noise, mostly surface waves are retrieved. The newly retrieved surface wave responses can be used to extract receiver-receiver phase velocities. These phase velocities often serve as input parameters for tomographic inverse problems. Another application of SI exploits the temporal stability of the multiply scattered arrivals of the newly retrieved surface wave responses. Temporal variations in the stability and/or arrival time of these multiply scattered arrivals can often be linked to temporally varying parameters such as hydrocarbon production and precipitation. For all applications, however, the accuracy of the retrieved responses is paramount. Correct response retrieval relies on a uniform illumination of the receivers: irregularities in the illumination pattern degrade the accuracy of the newly retrieved responses. In practice, the illumination pattern is often far from uniform. In that case, simple crosscorrelation of separate receiver recordings only yields an estimate of the actual, correct virtual-source response. Reformulating the theory underlying SI by crosscorrelation as a multidimensional deconvolution (MDD) process, allows this estimate to be improved. SI by MDD corrects for the non-uniform illumination pattern by means of a so-called point-spread function (PSF), which captures the irregularities in the illumination pattern. Deconvolution by this PSF removes the imprint of the irregularities on the responses obtained through simple crosscorrelation. We apply SI by MDD to surface wave data recorded by the Malargüe seismic array in western Argentina. The aperture of the array is approximately 60 km and it is located on a plateau just east of the Andean mountain range. The array has a T-shape: the receivers along one of the two lines act as virtual sources whose responses are recorded by the receivers along the other (perpendicular) line. We select time windows dominated by surface wave noise travelling in a favourable direction, that is, traversing the line of virtual sources before arriving at the receivers at which we aim to retrieve the virtual-source responses. These time windows are selected through a frequency-dependent slowness analysis along the two receiver lines. From the selected time windows, estimates of virtual-source responses are retrieved by means of crosscorrelations. Similarly, crosscorrelations between the positions of the virtual sources are computed to build C The

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