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.

Inter-well measurements are used to reduce drilling costs with no reduce small kimberlite body detection. The radio wave method enables measurement of the apparent absorption coefficient that is proportional to the effective electrical resistance of the rock. Our point is to build a three-dimensional model of distribution of electrical properties of inter-well space throughout the entire exploration region. The measured data is distributed unevenly because data points are grouped along the linear clusters. The distance between neighbor points composing a cluster is much smaller than distance between clusters. In terms of geostatistics, this means a significant spatial anisotropy of data distribution that is difficult to take into account using standard geostatistical approach. We have shown that the problem could be solved by methods developed within the theory of machine learning. To build a three-dimensional model of attenuation coefficient we used a modified method of -nearest neighbors.KEYWORDS: inter-well scanning; radio wave survey; machine learning; kNN-algorithm.Citation: Aleshin, I. M. and I. V. Malygin (2019), Machine learning approach to inter-well radio wave survey data imaging, Russ.

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