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Volume 19, Issue 1
  • ISSN: 1569-4445
  • E-ISSN: 1873-0604

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.

© 2021 European Association of Geoscientists & Engineers © 2020 European Association of Geoscientists & Engineers
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/content/journals/10.1002/nsg.12129
2021-01-20
2024-04-25

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Transient electromagnetic inversion based on particle swarm optimization and differential evolution algorithm
Near Surface Geophysics 19, 59 (2021); https://doi.org/10.1002/nsg.12129
/content/journals/10.1002/nsg.12129
/content/journals/10.1002/nsg.12129
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  • Article Type: Research Article
Keyword(s): Differential evolution; Inversion; Joint algorithm; Particle swarm optimization

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