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Different geophysical methods use different model parameterizations and inversion algorithms. Thus, combining these different inversion systems and yet adding the nonlinear cross‐gradient constraint in a joint inversion framework might be a big challenge, for instance, as explained further by Moorkamp et al. in 2011, there is a complex interaction between the data misfit terms, regularization and cross‐gradient terms and an imperfect fit to the data is expected. In this paper, we use a sequential algorithm for a two‐dimensional joint inversion of gravity and magnetic data, which tries to avoid these issues by decoupling the gravity inversion, the magnetic inversion and the cross‐gradient minimization processes. The efficiency of the algorithm and developed code is demonstrated by the joint inversion of noisy synthetic data. The results show a significant improvement in the respective models obtained by introducing the cross‐gradient joint inversion over the models obtained by separate inversions for synthetic data and then for field data targeting potash ore source in the AjiChai salt deposit in north‐western Iran. In this application case, the lower density of salt minerals such as potash, compared to its surrounding sedimentary sequences, motivates a gravity study. Furthermore, the relative lower susceptibility of these salt minerals, alongside their diamagnetic effect, makes them a suitable target for magnetic surveys. Separate gravity and magnetic studies had been performed over the deposit; however, a constitutive relationship between density and magnetization within the area of interest supporting a joint inversion had not been established. In this paper, we apply the sequential cross‐gradient approach to perform the first full joint inversion for the AjiChai salt deposit. The magnetic inversion here is performed to recover the magnetization amplitudes rather than the magnetization vector. In fact, we assume there is no remanent magnetization and, therefore, that the magnetization vector is constant and parallel to the geomagnetic field direction. The constructed density and magnetization models are of high concordance with available geological information and previous studies including drilling results. In addition, unlike previous separate inversion models, the models are structurally and geometrically similar. [ABSTRACT FROM AUTHOR]

In this paper we perform a 2-D joint inversion of DC resistivity and magnetic data, constrained by cross-gradients. Inspired by methods developed for potential fields, we introduce into both the separate and joint inversion algorithms also compactness and depth weighting functions, under the form of a model weighting-function. These constraints, usually not considered for DC resistivity inversion, reveal to be decisive for its joint inversion with magnetic data. A linear approximated forward problem of the resistivity is used for the joint inversion so that both the resistivity and magnetic problems are expressed as a linear integral equation under the form of a Fredholm integral of 1st kind. To examine the feasibility of the joint inversion algorithm, we first test the method with two synthetic cases: a thick dyke in a two-layered medium and a cavity located above a conductor. A third synthetic case involves a multisource model. The results are encouraging, revealing that the cross-gradient constraint is an effective tool to improve the separate inversions of DC resistivity and magnetic data. The joint inversion algorithm is also applied to data in the archeological area of the old Pompeii city, nearby Naples. Comparing the results of joint and separate inversions, we obtain a significant improvement in the interpretation of both kind of data in terms of buried walls of an ancient roman villa. In all the studied cases, the cross-gradient constraint appears to be a key-diagnostic tool to assess whether actual coherence is gained among DC resistivity and magnetic susceptibility models. [ABSTRACT FROM AUTHOR]

This paper presents a nature-based algorithm, titled multivariable teaching-learning-based optimization (MTLBO) algorithm. MTLBO algorithm during an iterative process can estimates the best values of the buried structure (model) parameters in a multi-objective problem. The algorithm works in two computa-tional phases: the teacher phase and the learner phase. The major purpose of the MTLBO algorithm is to modify the value of the learners and thus, improving the value of the model parameters which leads to the optimal solution. The variables of each learner (model) are the depth (z), amplitude coefficient (k), shape factor (q), angle of effective magnetization (Î,) and axis location (x0) parameters. We employ MTLBO method for the magnetic anomalies caused by the buried structures with a simple geometric shape such as sphere and horizontal cylinder. The efficiency of the MTLBO is also studied by noise corruption synthetic data, as the acceptable results were obtained. We have applied the MTLBO for the interpretation of the four magnetic anomaly profiles from Iran, Brazil and India. [ABSTRACT FROM AUTHOR]