YangNet: a nonlinear and nonstationary spatial interpolation method based on spatial compound variable theory.
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| Title: | YangNet: a nonlinear and nonstationary spatial interpolation method based on spatial compound variable theory. |
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| Authors: | Yang, Jie1 (AUTHOR), Liu, Qiliang1 (AUTHOR) qiliang.liu@csu.edu.cn, Mao, Xiancheng1 (AUTHOR), Liu, Zhankun1 (AUTHOR), Deng, Min1 (AUTHOR) |
| Source: | International Journal of Geographical Information Science. Jan2026, Vol. 40 Issue 1, p112-139. 28p. |
| Subjects: | Nonlinear statistical models, Graph neural networks, Stochastic processes, Spatial analysis (Statistics), Geographic spatial analysis, Geomatics |
| Geographic Terms: | China |
| Abstract: | Modeling nonstationary and nonlinear variations in spatial processes is challenging. To overcome this long-standing challenge in spatial interpolation, we introduced the spatial compound variable theory, which assumes that a spatial variable can be divided into global regular and local irregular components. We argue that nonstationary and nonlinear variations in these two components have distinct properties: the global regular component describes a nonlinear trend that is locally predictable, whereas the local irregular component represents local hotspots, cold spots, or outliers. Following the spatial compound variable theory, we developed a novel nonlinear and nonstationary spatial interpolation model by integrating Yang Chizhong filtering and an inductive graph convolution network (YangNet). Specifically, we used the Yang Chizhong filtering and interpolation method to estimate the global regular component with a nonlinear trend, built an inductive graph convolution network to model the nonlinear relationship between the estimated global regular component and the corresponding observation data, and used the nonlinear relationship to estimate the local irregular component. A case study of the Xiadian gold deposit in China demonstrates that YangNet outperforms four representative gold grade interpolation methods in terms of accuracy and smoothing effect mitigation. YangNet exhibits excellent adaptability and can be applied widely in geoscience. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Modeling nonstationary and nonlinear variations in spatial processes is challenging. To overcome this long-standing challenge in spatial interpolation, we introduced the spatial compound variable theory, which assumes that a spatial variable can be divided into global regular and local irregular components. We argue that nonstationary and nonlinear variations in these two components have distinct properties: the global regular component describes a nonlinear trend that is locally predictable, whereas the local irregular component represents local hotspots, cold spots, or outliers. Following the spatial compound variable theory, we developed a novel nonlinear and nonstationary spatial interpolation model by integrating Yang Chizhong filtering and an inductive graph convolution network (YangNet). Specifically, we used the Yang Chizhong filtering and interpolation method to estimate the global regular component with a nonlinear trend, built an inductive graph convolution network to model the nonlinear relationship between the estimated global regular component and the corresponding observation data, and used the nonlinear relationship to estimate the local irregular component. A case study of the Xiadian gold deposit in China demonstrates that YangNet outperforms four representative gold grade interpolation methods in terms of accuracy and smoothing effect mitigation. YangNet exhibits excellent adaptability and can be applied widely in geoscience. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 13658816 |
| DOI: | 10.1080/13658816.2025.2508840 |
