Zhang Fen, Tian Jiuyun, Lv Panpan, Luo Kaiyun, Huang Yonggu, Yang Shaohui, Deng Fei
College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, 610059, China.
Acquisition Technology Institute, BGP Inc., CNPC, Zhuozhou, 072750, China.
Sci Rep. 2025 Jul 14;15(1):25391. doi: 10.1038/s41598-025-11071-1.
As oil and gas exploration advances, the growing complexity of geological conditions demands higher-quality quadrilateral meshes for spectral element method-based seismic simulations. For complex geological models, existing quadrilateral meshing algorithms struggle to generate high-quality meshes that meet the spectral element method's requirements, often producing initial meshes with topological errors or concave elements, which compromise simulation accuracy. To address this, we propose a swarm intelligence-based secondary optimisation method, employing particle swarm optimisation (PSO), wolf pack algorithm (WPA), and firefly algorithm (FA) to iteratively refine distorted nodes. Results demonstrate that all three algorithms eliminate initial mesh defects, with WPA achieving the highest mesh quality, PSO exhibiting the fastest convergence, and FA performing least effectively. The optimised meshes meet the high-quality standards of the spectral element method, significantly improving simulation stability and computational efficiency, and laying a foundation for the further application of the spectral element method in seismic exploration.
随着油气勘探的推进，地质条件日益复杂，这就要求在基于谱元法的地震模拟中使用更高质量的四边形网格。对于复杂地质模型，现有的四边形网格划分算法难以生成满足谱元法要求的高质量网格，常常产生具有拓扑错误或凹面单元的初始网格，这会影响模拟精度。为了解决这个问题，我们提出了一种基于群体智能的二次优化方法，采用粒子群优化算法（PSO）、狼群算法（WPA）和萤火虫算法（FA）来迭代优化变形节点。结果表明，这三种算法都消除了初始网格缺陷，其中狼群算法获得的网格质量最高，粒子群优化算法收敛速度最快，萤火虫算法效果最差。优化后的网格符合谱元法的高质量标准，显著提高了模拟稳定性和计算效率，为谱元法在地震勘探中的进一步应用奠定了基础。
