论文标题
部分可观测时空混沌系统的无模型预测
Geomagnetic Survey Interpolation with the Machine Learning Approach
论文作者
论文摘要
本文描绘了无人机磁力测定数据插值的方法。该方法可容纳以下事实：这种数据沿一系列直线（类似于海事钉）具有样品的空间分布，这是多种无人机调查的重要特征。插值依赖于非常基本的最近的邻居算法，尽管使用了机器学习方法增强。通过智能调整最近的邻居算法参数，这种方法使误差少于5％。该方法通过Borok地磁天文台无人机航空磁性调查数据对地磁数据进行了试验测试。
This paper portrays the method of UAV magnetometry survey data interpolation. The method accommodates the fact that this kind of data has a spatial distribution of the samples along a series of straight lines (similar to maritime tacks), which is a prominent characteristic of many kinds of UAV surveys. The interpolation relies on the very basic Nearest Neighbours algorithm, although augmented with a Machine Learning approach. Such an approach enables the error of less than 5 percent by intelligently adjusting the Nearest Neighbour algorithm parameters. The method was pilot tested on geomagnetic data with Borok Geomagnetic Observatory UAV aeromagnetic survey data.
