1D Anisotropic Surface Wave Tomography with Bayesian Inference

TL;DR

This paper introduces a Bayesian framework for 1D anisotropic surface wave tomography, providing probabilistic models and uncertainty estimates, applied to synthetic data simulating upper mantle structures.

Contribution

It reformulates the second step of anisotropic surface wave tomography into a Bayesian inference problem, enabling probabilistic solutions and uncertainty quantification.

Findings

The Bayesian approach captures key features of the upper mantle.

Results depend on wavefield wavelength and model parameterization.

Additional constraints improve model regularization.

Abstract

Classically, anisotropic surface wave tomography is treated as an optimisation problem where it proceeds through a linearised two-step approach. It involves the construction of 2D group or phase velocity maps for each considered period, followed by the inversion of local dispersion curves inferred from these maps for 1D depth-functions of the elastic parameters. Here, we cast the second step into a fully Bayesian probability framework. Solutions to the inverse problem are thus an ensemble of model parameters (\textit{i.e.} 1D elastic structures) distributed according to a posterior probability density function and their corresponding uncertainty limits. The method is applied to azimuthally-varying synthetic surface wave dispersion curves generated by a 3D-deforming upper mantle. We show that such a procedure captures essential features of the upper mantle structure. The robustness of these features however strongly depend on the wavelength of the wavefield considered and the choice of the model parameterisation. Additional information should therefore be incorporated to regularise the problem such as the imposition of petrological constraints to match the geodynamic predictions.