Geomagnetic Survey Interpolation with the Machine Learning Approach
Author:
Aleshin Igor12, Kholodkov Kirill3, Malygin Ivan4, Shevchuk Roman2, Sidorov Roman2
Affiliation:
1. Schmidt Institute of the Physics of the Earth Russian Academy of Sciencies 2. Geophysical Center of the Russian Academy of Sciences 3. Schmidt Institute of Physics of the Earth of the Russian Academy of Sciences 4. Schmidt Institute of Physics of the Earth RAS
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.
Publisher
Geophysical Center of the Russian Academy of Sciences
Subject
General Earth and Planetary Sciences
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