Machine learning approach to inter-well radio wave survey data imaging

Aleshin, I.M.; Malygin, I.V.

Статья опубликована в журнале из списка RSCI Web of Science

Информация о цитировании статьи получена из Web of Science, Scopus
Статья опубликована в журнале из перечня ВАК
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 14 августа 2019 г.

Авторы: Aleshin I.M., Malygin I.V.
Журнал: Russian Journal of Earth Sciences
Том: 19
Номер: ES3003
Год издания: 2019
Издательство: Geophysical Center of the Russian Academy of Sciences
Местоположение издательства: Moscow
Первая страница: 1
Последняя страница: 6
DOI: 10.2205/2019es000664
Аннотация: 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-nearest neighbors.
Добавил в систему: Алёшин Игорь Михайлович

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Machine learning approach to inter-well radio wave survey data imagingстатья

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