Geomagnetic Survey Interpolation with the Machine Learning Approach

Igor Aleshin; Kirill I. Kholodkov; Ivan Malygin; Roman Shevchuk; Roman Sidorov

doi:doi:10.2205/2022ES000818

BISAC SCI019000

Geomagnetic Survey Interpolation with the Machine Learning Approach

Published в Russian Journal of Earth Sciences · Volume 22, Issue 6, 2022 · Pages 1–6 · Rubrics: ORIGINAL ARTICLES

DOI 10.2205/2022ES000818

Received: 20.09.2022 Accepted: 26.10.2022 Published: 07.12.2022 Language of publication: RUS

Authors

Igor Aleshin ¹ iD · Kirill I. Kholodkov ² iD · Ivan Malygin ³ · Roman Shevchuk ⁴ · Roman Sidorov ⁵

1 Schmidt Institute of the Physics of the Earth Russian Academy of Sciencies
, Geophysical Center of the Russian Academy of Sciences
, Fedorov Institute of Applied Geophysics
Moscow, Moscow, Russian Federation

2 Schmidt Institute of the Physics of the Earth Russian Academy of Sciencies
, Fedorov Institute of Applied Geophysics
Russian Federation

3 Schmidt Institute of Physics of the Earth RAS
Russian Federation

4 Geophysical Center of the Russian Academy of Sciences

5 Geophysical Center of the Russian Academy of Sciences

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 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.

KEYWORDS

aeromagnetic survey UAV machine learning k-NN Borok observatory

Information Text References

Authors:

1. Schmidt Institute of the Physics of the Earth Russian Academy of Sciencies

Geophysical Center of the Russian Academy of Sciences

Fedorov Institute of Applied Geophysics

Moscow, Moscow, Russian Federation

2. Schmidt Institute of the Physics of the Earth Russian Academy of Sciencies

Fedorov Institute of Applied Geophysics

Russian Federation

3. Schmidt Institute of Physics of the Earth RAS

Russian Federation

4. Geophysical Center of the Russian Academy of Sciences

5. Geophysical Center of the Russian Academy of Sciences

Type:

Article

Pages:

from 1 to 6

Status:

Published

Subject area:

BISAC SCI019000 Earth Sciences / General

Language:

English

Keywords:

aeromagnetic survey, UAV, machine learning, k-NN, Borok observatory

Funding

The work was carried out within the framework of the state assignment of the Schmidt Institute of Physics of the Earth of the Russian Academy of Sciences (IPE RAS) and Geophysical Center of the Russian Academy of Sciences (GC RAS), approved by the Ministry of Education and Science of the Russian Federation. The collaboration between the institutes was enabled by the separate agreement on joint scientific activities between the IPE RAS and the GC RAS. During the research, the equipment of IPE RAS and GC RAS was used.

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References

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