Geomagnetic Survey Interpolation with the Machine Learning Approach

TL;DR

This paper introduces a machine learning-enhanced interpolation method for UAV geomagnetic survey data, effectively handling spatial line-based sampling and achieving less than 5% error in pilot tests.

Contribution

It presents a novel approach combining Nearest Neighbours with machine learning to improve interpolation accuracy for UAV geomagnetic data.

Findings

Achieved less than 5% interpolation error.

Successfully applied to Borok Geomagnetic Observatory data.

Enhanced Nearest Neighbours with ML for better spatial interpolation.

Abstract

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.