Deep convolutions for in-depth automated rock typing

The description of rocks is one of the most time-consuming tasks in the everyday work of a geologist, especially when very accurate description is required. We here present a method that reduces the time needed for accurate description of rocks, enabling the geologist to work more efficiently. We de...

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Bibliographic Details
Published in:Computers & geosciences 2020-02, Vol.135, p.104330, Article 104330
Main Authors: Baraboshkin, Evgeny E., Ismailova, Leyla S., Orlov, Denis M., Zhukovskaya, Elena A., Kalmykov, Georgy A., Khotylev, Oleg V., Baraboshkin, Evgeny Yu, Koroteev, Dmitry A.
Format: Article
Language:English
Subjects:
Convolutional neural networks
Core image
Description
Geology
Lithotypes
Representation
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Summary:The description of rocks is one of the most time-consuming tasks in the everyday work of a geologist, especially when very accurate description is required. We here present a method that reduces the time needed for accurate description of rocks, enabling the geologist to work more efficiently. We describe the application of methods based on color distribution analysis and feature extraction. Then we focus on a new approach, used by us, which is based on convolutional neural networks. We used several well-known neural network architectures (AlexNet, VGG, GoogLeNet, ResNet) and made a comparison of their performance. The precision of the algorithms is up to 95% on the validation set with GoogLeNet architecture. The best of the proposed algorithms can describe 50 m of full-size core in 1 min. [Display omitted]
ISSN:0098-3004
1873-7803
DOI:10.1016/j.cageo.2019.104330