Transdimensional Bayesian inversion of magnetotelluric data in anisotropic layered media with galvanic distortion correction

Abstract

SUMMARY Presence of electrical anisotropy in the lithosphere can provide useful constraints on regional structure patterns and dynamics of tectonic processes, and they can be imaged by magnetotelluric (MT) data. However, Inversion of MT data for anisotropic structures using standard gradient-based approaches requires subjective choices of model regularization for constraining structure and anisotropy complexity. Furthermore, the ubiquitous presence of galvanic distortion due to small-scale near-surface conductivity inhomogeneities prevents accurate imaging of subsurface structures if ignored or not properly removed. Here, we present a transdimensional Bayesian approach for inverting MT data in layered anisotropic media. The algorithm allows flexible model parametrization, in which both the number of layers and model parameters of each layer are treated as unknowns. In this manner, the presence or absence of anisotropy within the layers, as well as the level of model complexity, is determined adaptively by the data. In addition, to account for the effects of galvanic distortion, three frequency-independent distortion parameters resulting from the distortion decomposition are treated as additional variables during the inversion. We demonstrate the efficiency of the algorithm to resolve both isotropic and anisotropic structures with synthetic and field MT data sets affected by galvanic distortion effects. The transdimensional inversion results for the field data are compatible with results from previous studies, and our results improve the constraints on the magnitude and the azimuth (i.e. most conductive direction) of electrically anisotropic structures. For practical applications, the validity of 1-D anisotropic approximation should be first tested prior to the use of our approach. Otherwise it may produce spurious anisotropic structures due to the inapplicability of the anisotropic 1-D inversion for MT data affected by 2-D or 3-D electrical resistivity structures.