Joint Inversion of Magnetotelluric and Surface Wave Data in an Anisotropic Earth

Joint inversion of different kinds of geophysical datasets has the potential to improve the model resolution. Joint inversions can involve datasets sensitive to the same physical parameter. This problem is even more challenging when datasets are sensitive to different physical parameters. Our work involves inverting simultaneously surface waves dispersion curves and long-period magnetotelluric measurements (sensitive to shear-wave velocity and electrical conductivity respectively) using a genetic algorithm. An approximate agreement between geoelectric strike and seismic fast axis direction in continental lithosphere has been found in various regions such as the Great Slave Lake shear zone (Eaton et al., 2004), across the Grenville Orogen (Ji et al., 1996) and the Sao Francisco Craton (Padilha et al., 2006). This suggests a common origin is likely for both seismic and electrical anisotropy. These observations motivate our attempt to jointly invert seismic and electrical anisotropic parameters. We simultaneously invert magnetotelluric data and Rayleigh waves dispersion curves in a anisotropic one-dimensional media. Assuming that seismic and electrical anisotropy have a common origin, we can thus expect superior resolution of azimuthal anisotropy for lithospheric and sub-lithospheric depths combining these two techniques. We have examined the capabilities and limitations of this new approach with synthetic datasets and obtained encouraging results. Another convincing way to validate the algorithm is to apply it to real datasets. However, finding a suitable site to apply this new method remains challenging as we need good quality MT and seismic measurements for coincident sites. Central Germany is a good candidate for such an inversion because of existing knowledge and data. Here, we present a first application of such a joint inversion in anisotropic media to a real dataset from Central Germany. We will focus on main assumptions in terms of compatibility and dimensionality needed for such a joint inversion. When these requirements are met, we will see how a joint inversion can improve the model resolution in comparison to separate inversions.