【摘要】 变差函数是量化空间相关性的重要工具，现有变差函数拟合方法存在拟合结果不稳定现象。针对现有方法的不足，本文提出一种基于深度学习的变差函数自动拟合方法，以提高自动拟合的精度与稳定性。实验变差函数的拟合本质是一种非线性优化问题，即实现实验变差函数与理论变差函数之间的匹配度最优化。新方法采用多组参数值不同的理论变差函数生成大量训练数据集并进行深度神经网络训练学习，最后采用训练模型完成实验变差函数自动拟合。多组实验结果表明，基于深度神经网络的强大拟合能力，新方法拟合稳定性、计算效率均优于最小二乘法，为地质统计学中变差函数自动拟合提供了新思路。更多还原

【Abstract】 A variogram serves as a crucial tool for quantifying spatial correlations. However, existing variogram fitting methods often yield unstable results. This study proposed an automatic variogram fitting method based on deep learning, aiming to enhance the precision and stability of automatic fitting. The fitting of the experimental variogram is essentially a nonlinear optimization problem, which involves optimizing the matching between the experimental and theoretical variograms. The proposed method generated substantial training datasets using several sets of theoretical variograms with varying parameter values for training and learning in deep neural networks. The trained model was then used for the automatic fitting of the experimental variogram. Multiple sets of experimental results demonstrate that based on the robust fitting capability of deep neural networks, the proposed method manifested superior fitting stability and computational efficiency compared to the least squares method, providing a novel approach for automatic variogram fitting in geostatistics.更多还原