Abstract
In this study, various algorithms and optimization methods were employed to solve the inverse problem of magnetotelluric (MT) sounding, including Differential Evolution, Basin-Hopping, Dual Annealing, SHGO, Nelder-Mead, COBYLA, Newton-CG, Newton-Raphson, and Hooke-Jeeves methods. These approaches were applied to identify and optimize both the global and local minima of the objective function, which quantifies the best fit between observed and theoretical MT data. By achieving an optimal agreement between measured and modeled datasets, these methods ensured high accuracy and reliability in the solution of the MT inverse problem.