Structural joint modeling of magnetotelluric data and Rayleigh wave dispersion curves using Pareto-based particle swarm optimization: An example to delineate the crustal structure of the southeastern part of the Biga Peninsula in western Anatolia

Abstract. It is well known that the joint inversion of magnetotelluric and seismological data sets improves the solution quality of the crustal structure, even if the electrical resistivity and seismic velocity parameters are not physically well correlated. The structurally coupled joint inversion approach has received much attention in the last two decades to estimate such parameters with penalizing their cross-gradient vectors at similar spatial positions. Despite this interest, various structural couplings and different physical directions (incremental or decremental) have been partially overlooked. We propose an approach for the joint inversion of magnetotelluric (MT) and Rayleigh wave dispersion (RWD) data to estimate uncorrelated parameters by integrating particle swarm optimization (PSO) and the Pareto optimality approach. We used these methods optimality to overcome difficulties encountered in traditional joint inversion algorithms and to obtain optimum solutions having same and/or different physical directions. The good correlation between the inverted and synthetic models produced noise-free and noisy data further strengthened our confidence in the modelling of the field data from the southeastern Biga Peninsula in western Anatolia. The models inverted from the field data, which are in consistent with previous studies, confirm the usefulness of the presented method. A remarkable feature of the presented method is the estimation of uncorrelated physical parameters such as electrical resistivity and seismic velocity without penalizing. Therefore, the presented method not only offers advantages in joint inversion but also allows modelers to observe and analyze model parameters having different sensitivities that may indicate different physical directions.