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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. p. 54-57 9303197 (Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Panferov, V, Tailakov, D & Donets, A 2020, Recognition of Rocks Lithology on the Images of Core Samples. in Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020., 9303197, Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020, Institute of Electrical and Electronics Engineers Inc., pp. 54-57, 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020, Virtual, Novosibirsk, Russian Federation, 14.11.2020. https://doi.org/10.1109/S.A.I.ence50533.2020.9303197

APA

Panferov, V., Tailakov, D., & Donets, A. (2020). Recognition of Rocks Lithology on the Images of Core Samples. In Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020 (pp. 54-57). [9303197] (Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/S.A.I.ence50533.2020.9303197

Vancouver

Panferov V, Tailakov D, Donets A. Recognition of Rocks Lithology on the Images of Core Samples. In Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 54-57. 9303197. (Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020). doi: 10.1109/S.A.I.ence50533.2020.9303197

Author

Panferov, Vladislav ; Tailakov, Dmitry ; Donets, Alexander. / Recognition of Rocks Lithology on the Images of Core Samples. Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 54-57 (Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020).

BibTeX

@inproceedings{813394aade954498977cd9084a8ca78e,
title = "Recognition of Rocks Lithology on the Images of Core Samples",
abstract = "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.",
keywords = "core sample, image segmentation, lithology, rock",
author = "Vladislav Panferov and Dmitry Tailakov and Alexander Donets",
note = "Funding Information: The Research was supported by the Russian Science Foundation (Project No. 19-79-30075). Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020 ; Conference date: 14-11-2020 Through 15-11-2020",
year = "2020",
month = nov,
day = "14",
doi = "10.1109/S.A.I.ence50533.2020.9303197",
language = "English",
series = "Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "54--57",
booktitle = "Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020",
address = "United States",

}

RIS

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