Recognition of Rocks Lithology on the Images of Core Samples

Vladislav Panferov, Dmitry Tailakov, Alexander Donets

Результат исследования: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике материалов конференциинаучнаярецензирование

Аннотация

Oil is one of the most important resources in the modern life. When an oil well is drilled, engineers extract the samples of core to analyze it and build the model of the geological formation. Now, the core samples and rock lithology segmentation is usually implemented by people by hand. Methods for the image segmentation and possible core samples segmentation approaches are reviewed. The novel dataset consisting of 69 images of segmented core samples created specifically for the task is presented in this paper. Also, two approaches for dataset creation were tried and described in this paper. The U-Net solution of the task with the first version of the dataset consisting of 4 classes and its results are described. Also the Mask R-CNN with ResNet-50 FPN model from the library Detectron2 with the second version of the dataset consisting of 11 classes of Argillite and Sandstone and its combination is described and results of experiments are provided.

Язык оригиналаанглийский
Название основной публикацииProceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020
ИздательInstitute of Electrical and Electronics Engineers Inc.
Страницы54-57
Число страниц4
ISBN (электронное издание)9780738131115
DOI
СостояниеОпубликовано - 14 ноя 2020
Событие2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020 - Virtual, Novosibirsk, Российская Федерация
Продолжительность: 14 ноя 202015 ноя 2020

Серия публикаций

НазваниеProceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020

Конференция

Конференция2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020
СтранаРоссийская Федерация
ГородVirtual, Novosibirsk
Период14.11.202015.11.2020

Предметные области OECD FOS+WOS

  • 1.02 КОМПЬЮТЕРНЫЕ И ИНФОРМАЦИОННЫЕ НАУКИ
  • 1.04 ХИМИЧЕСКИЕ НАУКИ

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