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<p>Extracting induced polarization (IP) information from transient electromagnetic (TEM) signals is crucial for the exploration of deep mineral, oil, and gas resources.. Linear inversion technology is the preferred method for extracting IP information, but it is associated with three primary drawbacks: dependence on the initial conditions, susceptibility to falling into a local optimum, and a significant lack of uniqueness. To solve the above problems, this study presents an improved shuffle frog leaping algorithm (ISFLA) that incorporates tent chaotic distribution and an adaptive mobile factor, which is employed to extract IP information. First, a tent chaotic operator is adopted to enhance the initial population distribution, thereby improving the global search capability. Then, an adaptive mobile factor is designed to replace the random operator, balancing local and global searches. This adjustment increases solution accuracy and ensures stable convergence in the later stages. Finally, TEM inversion for a 1D layered geoelectric model with IP information is performed using the proposed ISFLA approach. The inversion results show that the ISFLA method can more effectively reconstruct the geoelectric structure, extract IP information, and exhibit greater robustness. Compared to other heuristic algorithms, the proposed method achieves superior global search ability and inversion accuracy, making it well-suited for IP information extraction.</p>

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A novel method based on improved SFLA for IP information extraction from TEM signals

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月7日

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  • Ruiyou Li,
  • Ruiheng Li,
  • Guang Li,
  • Yong Zhang,
  • Xiaohui Ding,
  • Long Zhang

摘要

Extracting induced polarization (IP) information from transient electromagnetic (TEM) signals is crucial for the exploration of deep mineral, oil, and gas resources.. Linear inversion technology is the preferred method for extracting IP information, but it is associated with three primary drawbacks: dependence on the initial conditions, susceptibility to falling into a local optimum, and a significant lack of uniqueness. To solve the above problems, this study presents an improved shuffle frog leaping algorithm (ISFLA) that incorporates tent chaotic distribution and an adaptive mobile factor, which is employed to extract IP information. First, a tent chaotic operator is adopted to enhance the initial population distribution, thereby improving the global search capability. Then, an adaptive mobile factor is designed to replace the random operator, balancing local and global searches. This adjustment increases solution accuracy and ensures stable convergence in the later stages. Finally, TEM inversion for a 1D layered geoelectric model with IP information is performed using the proposed ISFLA approach. The inversion results show that the ISFLA method can more effectively reconstruct the geoelectric structure, extract IP information, and exhibit greater robustness. Compared to other heuristic algorithms, the proposed method achieves superior global search ability and inversion accuracy, making it well-suited for IP information extraction.