Trans-dimensional Bayesian joint inversion of magnetotelluric and geomagnetic depth sounding responses to constrain mantle electrical discontinuities

Abstract

Summary Joint inversion of magnetotelluric (MT) and geomagnetic depth sounding (GDS) responses can constrain the crustal and mantle conductivity structures. Previous studies typically use either deterministic inversion algorithms that provide limited information on model uncertainties or using stochastic inversion algorithms with a predetermined number of layers that is generally not known a priori. Here, we present a new open-source Bayesian framework for the joint inversion of MT and GDS responses to probe one-dimensional (1D) layered Earth’s conductivity structures. Within this framework, model uncertainties can be accurately estimated by generating numerous models that fit the observed data. A trans-dimensional Markov chain Monte Carlo (MCMC) method is employed to self-parametrize the model parameters, where the number of layers is treated as an inversion parameter that is determined automatically by the data. This adaptability can overcome the under or over-parametrization problem and may be able to automatically detect the conductivity discontinuities in the Earth’s interior. To accelerate the computations, a large number of Markov chains with different initial states can be run simultaneously using the MPI parallel technique. Synthetic data sets are used to validate the feasibility of our method and illustrate how separate and joint inversions, as well as various priors affect the posterior model distributions. The trans-dimensional MCMC algorithm is then applied to jointly invert the MT and GDS responses estimated at the Tucson geomagnetic observatory, North America. Our results not only contain model uncertainty estimates but also indicate two distinct conductivity discontinuities at around 85 and 440 km, which are likely related to the lithosphere-asthenosphere boundary and the upper interface of the mantle transition zone, respectively.

Hydrogen Diffusion in the Lower Mantle Revealed by Machine Learning Potentials

“…We find that the contribution of proton for Earth‐relevant water contents is comparable to the sum of all other three conduction mechanisms in bridgmanite (i.e., the conductivity of dry bridgmanite) when hydrogen is incorporated as (Mg + 2H) Si or (Al + H) Si defects. The 1D conductivity‐depth profiles in the lower mantle, as determined from electromagnetic induction studies, are shown in Figure 8b (Chen et al, 2022; Civet et al., 2015; Civet & Tarits, 2013; Grayver et al., 2017; Kuvshinov & Olsen, 2006; Olsen, 1999a, 1999b; Puthe et al., 2015; Velimsky, 2010; Verhoeven et al., 2021; Yao, Ren, Pan, et al, 2023; Yao, Ren, Tang, et al, 2023). Most of these conductivity data fall within two orders of magnitude (0.1–10 S m −1 ) and generally remain nearly constant or slightly increase with increasing depth.…”

Bayesian Inversion of Frequency-Domain Airborne EM Data With Spatial Correlation Prior Information

“…The widely popular trans-dimensional Markov chain Monte Carlo (MCMC) method [24][25] realizes this inversion process. This method is widely used in the probabilistic inversion of geophysical electromagnetic data, including 1D layered models [26][27][28][29][30][31][32][33][34] and 2D Voronoi cells [35][36][37]. However, this method is computationally complex and expensive when extended to 3D Voronoi cells [38][39].…”

Efficient Trans-dimensional Bayesian inversion of C-response data from geomagnetic observatory and satellite magnetic data

“…The conductivity structure of the Earth's mantle provides important information about the geodynamic state of the Earth's mantle (Zhan et al, 2007;Karato, 2010;Weng et al, 2017;Chen et al, 2020Chen et al, , 2023Yao et al, 2023b). Geomagnetic data either from geomagnetic observatories or satellites typically covering a period range from several hours to several hundred days are sensitive to the conductivity structure of the Earth's mantle (Olsen, 1999).…”

“…In the most recent work (Yao et al, 2023b), they developed a joint trans-D inversion based on an essentially single-chain sampling algorithm and physical parameter space for magnetotelluric data and local geomagnetic data from observatories. In their work, the model parameterization is treated as unknown and included in the inversion, determined by the data itself.…”