Journal of Jilin University(Earth Science Edition) ›› 2024, Vol. 54 ›› Issue (3): 993-1002.doi: 10.13278/j.cnki.jjuese.20230069

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Multi-Parameter Extraction of Superparamagnetic Effect Based on Improved Particle Swarm Optimization

Liu Huaishi,Zhao Xuejiao,Liu Yuxin,Fang Tingrui,Zhang Jing,Ji Yanju   

  1. College of Instrumentation and Electrical Engineering,Jilin University,Changchun 130026,China
  • Online:2024-05-26 Published:2024-05-26
  • Supported by:
    Supported by the National Natural Science Foundation of China (42104140,42030104), the Natural Science Foundation of Jilin Province (20210101475JC,YDZJ202101ZYTS023)  and the Innovation and Entrepreneurship Training Program of Jilin University (202110183274)

Abstract:  Superparamagnetic (SPM) effects in  timedomain electromagnetic  method is generally caused by magnetic anomalies or magnetic layers, and cause the slow diffusion phenomenon of approximately1 power law decay in the middle and late stages of the response. Meanwhile, it is also an important representation of the underground magnetic media information. However, ignoring the SPM effects will lead to an incorrect data interpretation using  traditional parameter extraction method to process the magnetic environment data. In order to interpret the SPM response accurately, this paper proposes a multiparameter extraction of SPM effects based on the improved particle swarm optimization (PSO) algorithm. The numerical simulation of the layered SPM effect is realized based on the ColeCole susceptibility model, and magnetic susceptibility and conductivity can affect the amplitude and attenuation slope of SPM responses. We improve the PSO algorithm with the particle velocity and position update strategy to realize the extraction of magnetic susceptibility, conductivity and other parameters. The results show that the maximum relative error of the proposed method is less than 2 % when extracting multiple parameters of SPM effect, which verifies the effectiveness of this method.


Key words: superparamagnetic effect, numerical simulation, particle swarm optimization, parameter extraction

CLC Number: 

  • P631.2
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