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.
@article{arxiv.2210.03379,
title = {Geomagnetic Survey Interpolation with the Machine Learning Approach},
author = {Igor Aleshin and Kirill Kholodkov and Ivan Malygin and Roman Shevchuk and Roman Sidorov},
journal= {arXiv preprint arXiv:2210.03379},
year = {2022}
}