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Earth Science Informatics
Published in: Earth Science Informatics 2/2023

28-02-2023 | RESEARCH

Automatic lithology identification method based on efficient deep convolutional network

Authors: Yan Guo, Zhuowu Li, Weihua Lin, Ji Zhou, Shixiang Feng, Luyu Zhang, Fujiang Liu

Published in: Earth Science Informatics | Issue 2/2023

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Abstract

Due to the complexity of the environment and the variability of rocks under natural conditions, it is difficult for geologists to obtain a rapid analysis and description of rocks. To this end, this study proposes a lithology identification method that is suitable for efficient computation and maintains good accuracy. The method uses deep learning and migration learning methods to build a lithology recognition model through PyTorch and YOLOv5 frameworks, and investigates the recognition of six types of rock data. The model achieves an accuracy of 90.30% on the validation set. The method was compared with five other commonly used methods which have the fewest network parameters and can recognise 176 rock images per second on a server (equipped with a Tesla T4 GPU).
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Metadata
Title
Automatic lithology identification method based on efficient deep convolutional network
Authors
Yan Guo
Zhuowu Li
Weihua Lin
Ji Zhou
Shixiang Feng
Luyu Zhang
Fujiang Liu
Publication date
28-02-2023
Publisher
Springer Berlin Heidelberg
Published in
Earth Science Informatics / Issue 2/2023
Print ISSN: 1865-0473
Electronic ISSN: 1865-0481
DOI
https://doi.org/10.1007/s12145-023-00962-4

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