A Novel Method Based on Improved SFLA for IP Information Extraction from TEM Signals

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

crossref（2024）

Jiangxi University of Finance and Economics | Hubei University Of Economics | East China University of Technology

Cited 0|Views2

Abstract

The extraction of induced polarization (IP) information from transient electromagnetic (TEM) signals holds significant practical importance for the development 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, an improved shuffle frog leaping algorithm (ISFLA) based on the tent chaotic distribution and an adaptive mobile factor is presented in this paper, and the algorithm is employed to extract IP information. First, a tent chaotic operator is adopted to ameliorate the initial population distribution to improve the global search capability. Then, an adaptive mobile factor is designed to replace the random operator for balancing the local and global search, which increases the solution accuracy and ensures stable convergence in the later period. Finally, TEM inversion for a 1-D layered geoelectric model with IP information is performed by the proposed ISFLA approach. The inversion results show that the ISFLA method can more effectively reconstruct the geoelectric structure as well as extract the IP information and achieve stronger robustness. Compared with other heuristic algorithm, the proposed algorithm achieves a superior global search ability and inversion accuracy, making it suitable for IP information extraction.

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【要点】：本文提出了一种基于帐篷混沌分布和自适应移动因子的改进 shuffled frog leaping algorithm（ISFLA），用于从TEM信号中提取IP信息，有效解决了线性反演技术存在的依赖初始条件、易陷入局部最优和结果不唯一等问题。

【方法】：通过采用帐篷混沌操作符优化初始种群分布，增强全局搜索能力，并设计自适应移动因子替代随机操作符，平衡局部和全局搜索，提高解的准确性和收敛稳定性。

【实验】：使用ISFLA方法对具有IP信息的1-D层状地电模型进行TEM反演，实验结果表明ISFLA能有效重构地电结构并提取IP信息，具有更强的鲁棒性。与其他启发式算法相比，ISFLA具有更优的全局搜索能力和反演精度。数据集为1-D层状地电模型。