Summary Surface waves extracted from ambient noise cross-correlations can be used to study depth variations of azimuthal anisotropy in the crust and upper mantle, complementing XKS splitting observations. In this work, we propose a novel approach based on beamforming to estimate azimuthal anisotropy of Rayleigh wave phase velocities extracted from ambient noise cross-correlations. This allows us to identify and remove measurements biased by wavefront deformation due to 3D heterogeneities, and to properly estimate uncertainties associated with observed phase velocities. In a second step, phase velocities measured at different periods can be inverted at depth with a transdimensional Bayesian algorithm where the presence or absence of anisotropy at different depths is a free variable. This yields a comprehensive probabilistic solution that can be exploited in different ways, in particular by projecting it onto a lower dimensional space, appropriate for interpretation. For example, we show the probability distribution of the integrated anisotropy over a given depth range (e.g. upper crust, lower crust). We apply this approach to recent data acquired across the AlpArray network and surrounding permanent stations. We show that only the upper crust has a large-scale coherent azimuthal anisotropy at the scale of the Alps with fast axis directions parallel to the Alpine arc, while such large-scale patterns are absent in the lower crust and uppermost mantle. This suggests that the recent Alpine history has only overridden the anisotropic signature in the upper crust, and that the deeper layers carry the imprint of older processes. In the uppermost mantle, fast directions of anisotropy are oriented broadly north-south, which is different from results from XKS-splitting measurements or long-period surface waves. Our results therefore suggest that XKS observations are related to deeper layers, the asthenosphere and/or subduction slabs. The area north-west of the Alps shows strong anisotropy in the lower crust and uppermost mantle with a fast axis in the north-east direction that could be related to Variscan deformation.
SUMMARY Coda-Q is used to estimate the attenuation and scattering properties of the Earth. So far focus has been on earthquake data at frequencies above 1 Hz, as the high noise level in the first and second microseismic peak, and possibly lower scattering coefficient, hinder stable measurements at lower frequencies. In this work, we measure and map coda-Q in the period bands 2.5–5 s, 5–10 s and 10–20 s in the greater Alpine region using noise cross-correlations between station pairs, based on data from permanent seismic stations and from the temporary AlpArray experiment. The observed coda-Q for short interstation distances is independent of azimuth so there is no indication of influence of the directivity of the incoming noise field on our measurements. In the 2.5–5 s and 5–10 s period bands, our measurements are self-consistent, and we observe stable geographic patterns of low and high coda-Q in the period bands 2.5–5 s and 5–10 s. In the period band 10–20 s, the dispersion of our measurements increases and geographic patterns become speculative. The coda-Q maps show that major features are observed with high resolution, with a very good geographical resolution of for example low coda-Q in the Po Plain. There is a sharp contrast between the Po Plain and the Alps and Apennines where coda-Q is high, with the exception a small area in the Swiss Alps which may be contaminated by the low coda-Q of the Po Plain. The coda of the correlations is too short to make independent measurements at different times within the coda, so we cannot distinguish between intrinsic and scattering Q. Measurements on more severely selected data sets and longer time-series result in identical geographical patterns but lower numerical values. Therefore, high coda-Q values may be overestimated, but the geographic distribution between high and low coda-Q areas is respected. Our results demonstrate that noise correlations are a promising tool for extending coda-Q measurements to frequencies lower than those analysed with earthquake data.
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