Deep Convolutions for In-Depth Automated Rock Typing

Show simple item record Baraboshkin E.E. Ismailova L.S. Orlov D.M. Zhukovskaya E.A. Kalmykov G.A. Khotylev O.V. Baraboshkin E.Yu. Koroteev E.A. 2023-09-11T08:05:44Z 2023-09-11T08:05:44Z 2020
dc.identifier.citation Computers & Geosciences, 2020, 135, 104330 ru_RU
dc.description.abstract 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 one minute. ru_RU
dc.language.iso en ru_RU
dc.subject Core Image ru_RU
dc.subject Description ru_RU
dc.subject Convolutional Neural Networks ru_RU
dc.subject Representation ru_RU
dc.subject Geology ru_RU
dc.subject Lithotypes ru_RU
dc.title Deep Convolutions for In-Depth Automated Rock Typing ru_RU
dc.type Article ru_RU
dc.identifier.doi 10.1016/j.cageo.2019.104330

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