A Reduced Order Approach for Probabilistic Inversions of 3D Magnetotelluric Data II: Joint Inversion of MT and Surface-Wave Data

Joint probabilistic inversions of magnetotelluric (MT) and seismic data have great potential for imaging the thermochemical structure of the lithosphere as well as mapping fluid/melt pathways and regions of mantle metasomatism. In this contribution, we present a novel probabilistic (Bayesian) joint inversion scheme for 3D MT and surface-wave dispersion data particularly designed for large-scale lithospheric studies. The approach makes use of a recently developed strategy for fast solutions of the 3D MT forward problem (Manassero et al., 2020, https://doi.org/10.1093/gji/ggaa415) and combines it with adaptive Markov chain Monte Carlo (MCMC) algorithms and parallel-in-parallel strategies to achieve extremely efficient simulations. To demonstrate the feasibility, benefits and performance of our joint inversion method for imaging the temperature and conductivity structures of the lithosphere, we apply it to two numerical examples of increasing complexity. The inversion approach presented here is timely and will be useful in the joint analysis of MT and surface wave data that are being collected in many parts of the world. This approach also opens up new avenues for the study of trans-lithospheric and trans-crustal magmatic systems, the detection of metasomatized mantle, and the incorporation of MT into multi-observable inversions for the physical state of the Earth's interior. Plain Language Summary The joint analysis of two or more geophysical data sets is becoming common practice for imaging the Earth's interior. This approach has great potential when the different data sets offer complementary sensitivities to the properties of interest. Such is the case of combining MT, that is, magnetotelluric (a technique based on observed electric and magnetic fields) and seismic data. In this context, probabilistic methods offer a powerful and general platform for the joint analysis of these data sets and their uncertainties. However, the inclusion of MT data into 3D probabilistic schemes has so far not been possible due to the significant computational cost of the MT problem. In this contribution we present a novel probabilistic joint analysis scheme for MT and surface-wave data which is based on a recently developed strategy to achieve fast and efficient solutions to the MT problem (Manassero et al., 2020, https://doi.org/10.1093/gji/ggaa415). We demonstrate the feasibility, benefits and performance of our approach for whole-lithosphere and upper mantle structures up to similar to 400 km depth. The approach presented here will be useful in the joint analysis of MT and seismic data that are being collected in many parts of the world.