# Geomagnetic Survey Interpolation with the Machine Learning Approach

arXiv: https://arxiv.org/abs/2210.03379 · HTML: https://mlsanity.com/paper/2210.03379/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 5 references that are in the map.
- First posted on arXiv 7 Oct 2022.

## Built on by (most cited first, 1 of 1)

- [Physics-informed Diffusion Generation for Geomagnetic Map Interpolation](https://mlsanity.com/paper/2602.00709/) (2026)

## Abstract (arXiv)

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.