This article presents a neural network approach for estimating the covariance function of spatial Gaussian random fields defined in a portion of the Euclidean plane. Our proposal builds upon recent contributions, expanding from the purely isotropic setting to encompass geometrically anisotropic correlation structures, i.e., random fields with correlation ranges that vary across different directions. We conduct experiments with both simulated and real data to assess the performance of the methodology and to provide guidelines to practitioners.

翻译：本文提出了一种神经网络方法，用于估计定义在欧几里得平面部分区域上的空间高斯随机场的协方差函数。我们的研究基于近期相关成果，从纯各向同性设定扩展到涵盖几何各向异性相关结构，即相关范围随方向变化的随机场。我们通过模拟数据和实际数据实验评估该方法的性能，并为实践者提供应用指南。