School of Geosciences, Yangtze University, Wuhan, Hubei, China.
School of Computer Science, Yangtze University, Jingzhou, Hubei, China.
PLoS One. 2022 Apr 22;17(4):e0266942. doi: 10.1371/journal.pone.0266942. eCollection 2022.
The Ordinary Kriging method is a common spatial interpolation algorithm in geostatistics. Because the semivariogram required for kriging interpolation greatly influences this process, optimal fitting of the semivariogram is of major significance for improving the theoretical accuracy of spatial interpolation. A deep neural network is a machine learning algorithm that can, in principle, be applied to any function, including a semivariogram. Accordingly, a novel spatial interpolation method based on a deep neural network and Ordinary Kriging was proposed in this research, and elevation data were used as a case study. Compared with the semivariogram fitted by the traditional exponential model, spherical model, and Gaussian model, the kriging variance in the proposed method is smaller, which means that the interpolation results are closer to the theoretical results of Ordinary Kriging interpolation. At the same time, this research can simplify processes for a variety of semivariogram analyses.
普通克里金法是地质统计学中常用的空间插值算法。由于克里金插值所需的半变异函数对这一过程有很大影响，因此对半变异函数的最佳拟合对于提高空间插值的理论精度具有重要意义。深度神经网络是一种机器学习算法，原则上可以应用于任何函数，包括半变异函数。因此，本研究提出了一种基于深度神经网络和普通克里金的新的空间插值方法，并以高程数据为例进行了研究。与传统的指数模型、球状模型和高斯模型拟合的半变异函数相比，该方法中的克里金方差更小，这意味着插值结果更接近普通克里金插值的理论结果。同时，本研究可以简化各种半变异函数分析的过程。
