交错各向异性与洞效应的通用参数化协方差函数类
Versatile Parametric Classes of Covariance Functions that Interlace Anisotropies and Hole Effects
AI总结：
本文提出可灵活整合各向异性与洞效应的新型参数化协方差函数构造，推广多类经典模型，并在含水层地球物理数据上取得更优空间预测。
摘要：
协方差函数是建模空间过程依赖结构的基本工具。本工作研究了协方差函数的新型构造，这些构造能够以复杂且通用的方式整合各向异性和洞效应，从而有可能更准确地表示真实数据中出现的依赖结构。我们表明，这些构造扩展了广泛使用的协方差模型，包括 Matérn、Cauchy、紧支撑超几何和基数正弦模型。我们将所得结果应用于来自岩石碳酸盐含水层的地球物理数据集，并证明与基础协方差模型相比，所提出的模型在未采样位置产生了更准确的预测。
英文摘要：
Covariance functions are a fundamental tool for modeling the dependence structure of spatial processes. This work investigates novel constructions for covariance functions that enable the integration of anisotropies and hole effects in complex and versatile ways, having the potential to provide more accurate representations of dependence structures arising with real-world data. We show that these constructions extend widely used covariance models, including the Matérn, Cauchy, compactly-supported hypergeometric and cardinal sine models. We apply our results to a geophysical data set from a rock-carbonate aquifer and demonstrate that the proposed models yield more accurate predictions at unsampled locations compared to basic covariance models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
