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Azimuthal anisotropy from eikonal tomography: example from ambient-noise measurements in the AlpArray network

Abstract : Ambient-noise records from the AlpArray network are used to measure Rayleigh wave phase velocities between more than 150 000 station pairs. From these, azimuthally anisotropic phase-velocity maps are obtained by applying the eikonal tomography method. Several synthetic tests are shown to study the bias in the Ψ2 anisotropy. There are two main groups of bias, the first one caused by interference between refracted/reflected waves and the appearance of secondary wave fronts that affect the phase traveltime measurements. This bias can be reduced if the amplitude field can be estimated correctly. Another source of error is related to the incomplete reconstruction of the traveltime field that is only sparsely sampled due to the receiver locations. Both types of bias scale with the magnitude of the velocity heterogeneities. Most affected by the spurious Ψ2 anisotropy are areas inside and at the border of low-velocity zones. In the isotropic velocity distribution, most of the bias cancels out if the azimuthal coverage is good. Despite the lack of resolution in many parts of the surveyed area, we identify a number of anisotropic structures that are robust: in the central Alps, we find a layered anisotropic structure, arc-parallel at mid-crustal depths and arc-perpendicular in the lower crust. In contrast, in the eastern Alps, the pattern is more consistently E-W oriented which we relate to the eastward extrusion. The northern Alpine forleand exhibits a preferential anisotropic orientation that is similar to SKS observations in the lowermost crust and uppermost mantle.
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Contributor : Nathalie POTHIER Connect in order to contact the contributor
Submitted on : Saturday, June 4, 2022 - 2:45:43 PM
Last modification on : Saturday, June 25, 2022 - 3:34:14 AM
Long-term archiving on: : Monday, September 5, 2022 - 6:29:03 PM


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E. D. Kästle, I. Molinari, L. Boschi, E. Kissling. Azimuthal anisotropy from eikonal tomography: example from ambient-noise measurements in the AlpArray network. Geophysical Journal International, 2022, 229, pp.151-170. ⟨10.1093/gji/ggab453⟩. ⟨insu-03595128⟩



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