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The PSO-IFAH optimization algorithm for transient electromagnetic inversion.

Zhengyu Xu1,2, Guofeng Zhao3,4, Xian Liao1

  • 1School of Electrical Engineering, Chongqing University, Chongqing, China.

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|January 22, 2025
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Summary
This summary is machine-generated.

A new hybrid optimization algorithm, PSO-IFAH, enhances transient electromagnetic (TEM) data processing. This method significantly improves calculation accuracy and efficiency for geophysical investigations, offering superior anti-noise properties and detailed underground structure depiction.

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Area of Science:

  • Geophysics
  • Computational Intelligence
  • Data Processing

Background:

  • Transient electromagnetic (TEM) method offers efficient, high-resolution, non-contact geophysical surveys.
  • Current TEM data processing methods lag, limiting applications in superficial engineering.
  • Swarm intelligence algorithms like PSO and FA have limitations in accuracy and efficiency.

Purpose of the Study:

  • To develop a refined data processing method for TEM surveys.
  • To improve the accuracy and efficiency of swarm intelligence algorithms for geophysical inversion.
  • To propose a novel hybrid optimization algorithm, PSO-IFAH, for enhanced TEM data interpretation.

Main Methods:

  • Modified the Firefly Algorithm (FA) by incorporating particle velocity concepts from Particle Swarm Optimization (PSO).
  • Developed an improved PSO-IFA hybrid optimization algorithm (PSO-IFAH).
  • Applied PSO, FA, and PSO-IFAH algorithms to inversion calculations using a layered model.

Main Results:

  • The PSO-IFAH algorithm demonstrated over 80% improvement in calculation accuracy and over 60% in efficiency compared to PSO and FA.
  • PSO-IFAH exhibited superior inversion accuracy, stability, and anti-noise capabilities.
  • Application to ground TEM data processing enhanced anomaly resolution and low-resistance detail depiction, aligning with excavation results.

Conclusions:

  • The PSO-IFAH algorithm significantly advances TEM data processing precision and interpretation.
  • The algorithm accurately depicts underground electrical structures and karst developments.
  • This hybrid approach overcomes limitations of existing swarm intelligence algorithms for geophysical applications.