Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Recognition of Rocks Lithology on the Images of Core Samples. / Panferov, Vladislav; Tailakov, Dmitry; Donets, Alexander.
Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020. Institute of Electrical and Electronics Engineers Inc., 2020. стр. 54-57 9303197 (Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
}
TY - GEN
T1 - Recognition of Rocks Lithology on the Images of Core Samples
AU - Panferov, Vladislav
AU - Tailakov, Dmitry
AU - Donets, Alexander
N1 - Funding Information: The Research was supported by the Russian Science Foundation (Project No. 19-79-30075). Publisher Copyright: © 2020 IEEE.
PY - 2020/11/14
Y1 - 2020/11/14
N2 - 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.
AB - 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.
KW - core sample
KW - image segmentation
KW - lithology
KW - rock
UR - http://www.scopus.com/inward/record.url?scp=85099545099&partnerID=8YFLogxK
U2 - 10.1109/S.A.I.ence50533.2020.9303197
DO - 10.1109/S.A.I.ence50533.2020.9303197
M3 - Conference contribution
AN - SCOPUS:85099545099
T3 - Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020
SP - 54
EP - 57
BT - Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020
Y2 - 14 November 2020 through 15 November 2020
ER -
ID: 34175981