A reduced order approach for probabilistic inversions of 3-D magnetotelluric data I: general formulation
Author (s): Manassero M.C., J.C. Afonso, F. Zyserman and S. Zlotnik
Journal: Geophysical Journal International
Volume: 223
Date: 2020
Abstract:
Simulation-based probabilistic inversions of 3D magnetotelluric (MT) data are arguably the best option to deal with the non-linearity and non-uniqueness of the MT problem. However, the computational cost associated with the modeling of 3D MT data has so far precluded the community from adopting and/or pursuing full probabilistic inversions of large MT datasets. In this contribution, we present a novel and general inversion frame- work, driven by Markov chain Monte Carlo (MCMC) algorithms, which combines i) an efficient parallel-in-parallel structure to solve the 3D forward problem, ii) a reduced order technique to create fast and accurate surrogate models of the forward problem, and iii) adaptive strategies for both the MCMC algorithm and the surrogate model. In particular, and contrary to traditional implementations, the adaptation of the surrogate is integrated into the MCMC inversion. This circumvents the need of costly offline stages to build the surrogate and further increases the overall efficiency of the method.
We demonstrate the feasibility and performance of our approach to invert for large-scale
conductivity structures with two numerical examples using different parameterizations
and dimensionalities. In both cases, we report staggering gains in computational efficiency
compared to traditional MCMC implementations. Our method finally removes the
main bottleneck of probabilistic inversions of 3D MT data and opens up new opportunities
for both stand-alone MT inversions and multi-observable joint inversions for the
physical state of the Earth’s interior.
