Russian Journal of Earth Sciences Russian Journal of Earth Sciences Russian Journal of Earth Sciences 1681-1208 55526 10.2205/2022ES000818 ОРИГИНАЛЬНЫЕ СТАТЬИ ORIGINAL ARTICLES ОРИГИНАЛЬНЫЕ СТАТЬИ Geomagnetic Survey Interpolation with the Machine Learning Approach Geomagnetic Survey Interpolation with the Machine Learning Approach https://orcid.org/0000-0001-7489-2951 Алёшин Игорь Михайлович Aleshin Igor Mikhaylovich ima@ifz.ru https://orcid.org/0000-0003-0324-9795 Холодков Кирилл И. Kholodkov Kirill I. keir@ifz.ru Малыгин Иван Malygin Ivan Шевчук Роман Shevchuk Roman Сидоров Роман Sidorov Roman Институт физики Земли им. О.Ю. Шмидта Российской академии наук Moscow Россия Schmidt Institute of Physics of the Earth of the Russian Academy of Sciences Moscow Russian Federation Геофизический центр РАН Москва Россия Geophysical Center of the Russian Academy of Sciences Moscow Russian Federation Институт физики Земли им. О.Ю. Шмидта Российской академии наук Россия Schmidt Institute of the Physics of the Earth Russian Academy of Sciencies Russian Federation Институт прикладной геофизики имени Е. К. Фёдорова Россия Fedorov Institute of Applied Geophysics Russian Federation Институт физики Земли им. О. Ю. Шмидта РАН Россия Schmidt Institute of Physics of the Earth RAS Russian Federation Геофизический центр РАН Москва Россия Geophysical Center of the Russian Academy of Sciences Moscow Russian Federation Геофизический центр РАН Москва Россия Geophysical Center of the Russian Academy of Sciences Moscow Russian Federation 07 12 2022 07 12 2022 22 6 1 6 20 09 2022 26 10 2022 https://rjes.ru/en/nauka/article/55526/view

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.

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 aeromagnetic survey UAV machine learning k-NN Borok observatory 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. 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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