Transient electromagnetic inversion based on particle swarm optimization and differential evolution algorithm

Abstract

ABSTRACT For transient electromagnetic inversion, a gradient‐based algorithm is strongly dependent on the quality of the initial model, while any non‐gradient‐based algorithm often falls too easily into local optima. This paper proposes a joint differential‐evolution–particle‐swarm‐optimization inversion algorithm, which provides a better global optimization. A dual‐population evolution strategy and information exchange mechanism is presented. For verification, this is followed by adoption of a layered inversion model in the transient electromagnetic inversion with a central loop. The results show that the differential‐evolution–particle‐swarm‐optimization joint algorithm can reduce the probability of a premature phenomenon (i.e. falling into local optima) and improve the inversion accuracy, efficiency and stability, with a fast convergence occuring in the early stages. Furthermore, the proposed algorithm has a higher degree of fitting (prediction ability) for data inversion and is feasible for transient electromagnetic inversion.