Bayesian analysis of azimuthal anisotropy in the Alpine lithosphere from beamforming of ambient noise cross-correlations

Abstract

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.

Anisotropic Reversible‐Jump McMC Shear‐Velocity Tomography of the Eastern Alpine Crust

“…Similar directions are found by Soergel et al. (2022) north of the Alps. This is very similar to the pattern of anisotropies from SKS splitting which are, however, mostly attributed to LPO of minerals in the asthenosphere (e.g., Barruol et al., 2011; Kummerow & Kind, 2006), although more complex interpretations involving various mechanism are also sometimes invoked (Löberich & Bokelmann, 2022; Qorbani et al., 2015).…”

“…This structure is related to a sinistral strike-slip movement due to the indentation of Adria (e.g., Scharf et al, 2013), which, however, would not necessarily rotate the pre-existing foliation or fold structures to a fault-parallel orientation. The fault-parallelism of the upper crustal anisotropy in the Alps is one of the main findings of this study and also in general agreement with the mapped fast axis in Schippkus et al (2020) and Soergel et al (2022). Even in SKS splitting results, there are indications for a fault-parallelism around the Giudicarie zone (e.g., Link & Rümpker, 2021).…”

Continuous isolated noise sources induce repeating waves in the coda of ambient noise correlations

“…A second master station in Poland (PL.OJC) illustrates how the converging (acausal) and diverging (causal) parts of the correlation wavefield depend on the geometry of array stations to master station and point roughly towards the great-circle between the two (Soergel et al, 2022), whereas the dominant directions towards West appear to be independent of the master station (Fig. 2).…”

Physics‐Informed Neural Networks for Elliptical‐Anisotropy Eikonal Tomography: Application to Data From the Northeastern Tibetan Plateau

“…Compared with earthquake‐based surface wave tomography, ambient noise tomography enables substantial additional coverage at short periods (Shapiro et al., 2005). Numerous array‐based tomographic methods have been developed to derive anisotropy from ambient noise, such as eikonal and Helmholtz tomography (Lin et al., 2009; Lin & Ritzwoller, 2011), wave gradiometry (Cao et al., 2020; De Ridder & Curtis, 2017) and beamforming (Soergel et al., 2023; Wu et al., 2023).…”

“…. Numerous array-based tomographic methods have been developed to derive anisotropy from ambient noise, such as eikonal and Helmholtz tomography (Lin et al, 2009;, wave gradiometry (Cao et al, 2020;De Ridder & Curtis, 2017) and beamforming (Soergel et al, 2023;Wu et al, 2023).…”