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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Combining Regression Kriging With Machine Learning Mapping for Spatial Variable Estimation
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Combining Regression Kriging With Machine Learning Mapping for Spatial Variable Estimation

机译：与机器学习映射结合回归克里格，用于空间变量估计

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摘要

Spatial variable estimation is a basic application of geostatistics. In general, this task is performed based on observations of limited points. For some cases, intensive observed data obtained from other sources are also available as the auxiliary variables. To utilize the auxiliary information in these data, methods such as regression kriging (RK) or cokriging are proposed. However, these methods all assume that the auxiliary variables keep linear correlation with the target variable implicitly, which is not satisfied in most cases. In this letter, through the combination of nonlinear machine learning mapping (MLM), we propose a novel hybrid method to relax the linear assumption of RK. The proposed method is applied to a real-world subsurface shale volume estimation task for demonstration. Compared with existing methods such as ordinary kriging, RK, and MLM, the relative estimation error reduction of the proposed method is larger than 10. Meanwhile, the estimation resolution is also improved. This indicates that the proposed method provides an alternative way for further spatial variable estimation practices.
机译：空间变量估计是地统计数据的基本应用。通常，该任务是基于有限点的观察来执行的。对于某些情况，从其他来源获得的密集观察数据也可用作辅助变量。为了利用这些数据中的辅助信息，提出了回归克里格（RK）或Cokriging等方法。但是，这些方法都假定辅助变量隐含地与目标变量保持线性相关性，这在大多数情况下都不满足。在这封信中，通过非线性机器学习映射（MLM）的组合，我们提出了一种新颖的混合方法来放松RK的线性假设。该方法应用于真实世界地下页岩体积估计任务以进行演示。与现有方法（如普通Kriging，RK和MLM）相比，所提出的方法的相对估计误差降低大于10。同时，估计分辨率也得到改善。这表明所提出的方法为进一步的空间可变估计实践提供了一种替代方法。

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