A Comparative Analysis of Three Computational-Intelligence Metaheuristic Methods for the Optimization of TDEM Data

Abstract We focus on the performances of three nature-inspired metaheuristic methods for optimization time-domain electromagnetic (TDEM) data: Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) algorithms. While GA PSO have been used in a plethora geophysical applications, GWO has received little attention literature so far, despite promising outcomes. This study directly quantitatively compares GA, applied to TDEM data. To date, these algorithms only compared pairs. The were first synthetic example noise-corrupted data then two field surveys carried out Italy. Real from survey refer sounding acquired groundwater prospection over known stratigraphy. set second deals with characterization geothermal reservoir. resulting resistivity models are provide thorough overview comparative analysis reveals that perform better than GA. yields highest misfit an ineffective minimization objective function. similar outcomes terms both distribution misfits, thus providing compelling evidence emerging established highly valid tools stochastic inverse modeling geophysics.