Russian Journal of Earth Sciences Russian Journal of Earth Sciences Russian Journal of Earth Sciences 1681-1208 46583 10.2205/2019ES000664 ОРИГИНАЛЬНЫЕ СТАТЬИ ORIGINAL ARTICLES ОРИГИНАЛЬНЫЕ СТАТЬИ Machine learning approach to inter-well radio wave survey data imaging Machine learning approach to inter-well radio wave survey data imaging Aleshin I M Aleshin I M ima@ifz.ru Малыгин Иван Malygin Ivan Schmidt Institute of Physics of the Earth RAS ru Schmidt Institute of Physics of the Earth RAS ru Институт физики Земли им. О. Ю. Шмидта РАН Россия Schmidt Institute of Physics of the Earth RAS Russian Federation 19 3 29 10 2021 https://rjes.ru/en/nauka/article/46583/view

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 k" role="presentation">k

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 k" role="presentation">k

 inter-well scanning radio wave survey machine learning kNN-algorithm inter-well scanning radio wave survey machine learning kNN-algorithm This work was supported by budget projects of the Schmidt Institute of Physics of the Earth, Russian Academy of Sciences, and the Laverov Federal Center for Integrated Arctic Research, Russian Academy of Sciences (project number AAAA-A18-118012490072-7).

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