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Quadrilateral mesh optimisation method based on swarm intelligence optimisation.

Fen Zhang1, Jiuyun Tian2, Panpan Lv3

  • 1College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, 610059, China.

Scientific Reports
|July 14, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces swarm intelligence algorithms to improve quadrilateral meshes for seismic simulations. The wolf pack algorithm (WPA) yielded the best mesh quality, enhancing simulation stability and efficiency in oil and gas exploration.

Keywords:
Forward modelingQuadrilateral meshSeismic explorationSpectral element methodSwarm intelligence algorithm

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Area of Science:

  • Geophysics
  • Computational Science
  • Materials Science

Background:

  • Advancing oil and gas exploration requires higher-quality quadrilateral meshes for spectral element method (SEM) seismic simulations.
  • Existing meshing algorithms struggle with complex geological models, producing defects that reduce simulation accuracy.

Purpose of the Study:

  • To develop a swarm intelligence-based secondary optimization method for refining quadrilateral meshes.
  • To evaluate the effectiveness of particle swarm optimization (PSO), wolf pack algorithm (WPA), and firefly algorithm (FA) in improving mesh quality for SEM.

Main Methods:

  • A swarm intelligence-based secondary optimization approach was employed.
  • Particle Swarm Optimization (PSO), Wolf Pack Algorithm (WPA), and Firefly Algorithm (FA) were used to iteratively refine distorted mesh nodes.
  • Optimized meshes were assessed for quality and suitability for SEM.

Main Results:

  • All tested algorithms (PSO, WPA, FA) successfully eliminated initial mesh defects.
  • WPA demonstrated the highest mesh quality, while PSO showed the fastest convergence.
  • FA was the least effective among the three algorithms.

Conclusions:

  • The optimized meshes satisfy the high-quality requirements of the spectral element method.
  • Improved mesh quality significantly enhances seismic simulation stability and computational efficiency.
  • This work provides a foundation for applying SEM in complex geological settings for seismic exploration.