doctoral candidate
Geophysical Center of the Russian Academy of Sciences
Russian Federation
Russian Federation
Russian Federation
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 neighbourss algorithm, although augmented with a Machine Learning approach. Such an approach enables the error of less than 5 percent by intelligently adjusting the nearest neighbours algorithm parameters. The method was pilot tested on geomagnetic data with Borok Geomagnetic Observatory UAV aeromagnetic survey data.
aeromagnetic survey, UAV, machine learning, k-NN, Borok observatory
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