Variation function is an important tool for describing the spatial correlation characteristics of regionalized variables in geostatistical methods. Variation function modeling is an important part of kriging interpolation and will directly affect the accuracy of the final interpolation result. The purpose of this work is to address the shortcomings of traditional variogram fitting methods, introduce particle swarm algorithm and artificial fish swarm algorithm under swarm intelligence framework, and design a variogram parameter fitting based on the hybrid algorithm of particle swarm and artificial fish swarm method. With this method, the minimum difference between the variation function fitting model and the given experimental variation value is utilized as the optimization goal. An appropriate objective function is set to convert it into a minimum problem. The hybrid algorithm has a strong search ability and convergence, as well as the ability to obtain the satisfactory fitness values. By comparing the results of the VARFIT fitting and the results of the optimization algorithm, it can be concluded that the absolute deviation of the fitting results of the optimization algorithm is 3.39 lower than the results of the VARFIT fitting. Compared with the traditional variogram modeling approach, this method has a strong optimization ability and high precision, and can effectively realize the automatic fitting of variogram parameters.

变异函数是描述地统计方法中区域变量空间相关性的重要工具。变异函数建模是克里金插值的重要组成部分，将直接影响最终插值结果的准确性。这项工作的目的是解决传统变异函数拟合方法的缺点，在种群智能框架下引入粒子群算法和人工鱼群算法，并基于粒子群和人工鱼群混合算法设计变异函数参数拟合。通过这种方法，将变异函数拟合模型与给定的实验变异值之间的最小差异用作优化目标。设置了适当的目标函数以将其转换为最小问题。混合算法具有很强的搜索能力和收敛性，以及获得令人满意的适应度值的能力。通过比较VARFIT拟合的结果和优化算法的结果，可以得出结论，优化算法的拟合结果的绝对偏差比VARFIT拟合的结果低3.39。与传统的方差图建模方法相比，该方法具有较强的优化能力和较高的精度，可以有效地实现方差图参数的自动拟合。通过比较VARFIT拟合的结果和优化算法的结果，可以得出结论，优化算法的拟合结果的绝对偏差比VARFIT拟合的结果低3.39。与传统的方差图建模方法相比，该方法具有较强的优化能力和较高的精度，可以有效地实现方差图参数的自动拟合。通过比较VARFIT拟合的结果和优化算法的结果，可以得出结论，优化算法的拟合结果的绝对偏差比VARFIT拟合的结果低3.39。与传统的方差图建模方法相比，该方法具有较强的优化能力和较高的精度，可以有效地实现方差图参数的自动拟合。