蚱蜢算法在瑞雷波频散曲线反演中的应用

摘要

As a new algorithm,the grasshopper optimization algorithm(GOA )belongs to swarm intelligent algorithms. It is inspired by the unique foraging behavior of grasshoppers at different stages.The movement of any grasshopper will be impacted by all the other grasshopper, which ensure the accuracy of convergence.In this paper,GOA is applied to invert surface-wave phase velocities. Using synthetic and real Rayleigh wave data, we examine the effectiveness and applicability of the GOA scheme in deducing an S-wave velocity profile for near-surface applications.The objective function in the proposed algorithm is proved to be able to rapidly converge to the global optimization solution.Another advantage of the proposed algorithm is a wide probability distribution of model parameters,which means this algorithm can define the scope of true-value and find the global minimum even in an extensive search space to guarantee the reliability of inversion results.%蚱蜢算法是一种新型的群智能优化算法,其灵感来源于蚱蜢在不同阶段表现出的独特觅食行为.该算法将蚱蜢算子的移动分为局部搜索与全局搜查两个阶段,算子每次移动均受其余所有算子的共同影响,以保证收敛到精确解.将蚱蜢算法引入面波频散曲线反演,以获得近地表横波速度.基于理论数据和实测瑞雷波数据,分析了利用蚱蜢算法计算近地表横波速度的有效性和适用性.目标函数解在反演迭代过程中能够快速收敛到全局最优;模型参数的分布概率高,即在寻找到全局最优解的同时,能够确保解中每个参数同时达到最优,保证了反演结果的可靠性.