Imaging anisotropic layering with Bayesian inversion of multiple data types

Abstract

Azimuthal anisotropy is a powerful tool to reveal information about both the present structure and past evolution of the mantle. Anisotropic images of the upper mantle are usually obtained by analysing various types of seismic observables, such as surface wave dispersion curves or waveforms, SKS splitting data, or receiver functions. These different data types sample different volumes of the earth, they are sensitive to different length scales, and hence are associated with different levels of uncertainties. They are traditionally interpreted separately, and often result in incompatible models. We present a Bayesian inversion approach to jointly invert these different data types. Seismograms for SKS and P phases are directly inverted using a cross-convolution approach, thus avoiding intermediate processing steps, such as numerical deconvolution or computation of splitting parameters. Probabilistic 1-D profiles are obtained with a transdimensional Markov chain Monte Carlo scheme, in which the number of layers, as well as the presence or absence of anisotropy in each layer, are treated as unknown parameters. In this way, seismic anisotropy is only introduced if required by the data. The algorithm is used to resolve both isotropic and anisotropic layering down to a depth of 350 km beneath two seismic stations in North America in two different tectonic settings: the stable Canadian shield (station FFC) and the tectonically active southern Basin and Range Province (station TA-214A). In both cases, the lithosphere-asthenosphere boundary is clearly visible, and marked by a change in direction of the fast axis of anisotropy. Our study confirms that azimuthal anisotropy is a powerful tool for detecting layering in the upper mantle.