Lithology identification method of cuttings based on improved VGG16

Jinrong Xiao

doi:10.1117/12.3004563

16 August 2023 Lithology identification method of cuttings based on improved VGG16

Jinrong Xiao

Proceedings Volume 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023); 127870E (2023) https://doi.org/10.1117/12.3004563
Event: 6th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE 2023), 2023, Shenyang, China

Abstract

Aiming at lithology identification of drilling cuttings, a lithology identification method based on improved VGG16 is proposed in this paper. Firstly, the rock image data are preprocessed, and the data are expanded by image segmentation, normalization and data enhancement. Secondly, the lithology recognition model is built with VGG16 as the backbone network, finally, the convolutional neural network is good at extracting local features, but it is difficult to obtain global information, by embedding SE attention module, the network can capture long-distance features. The experimental results show that the improved VGG16 model has an accuracy of 90.32%, which is 1.65% higher than that of VGG16 network. It shows that the improved VGG16 network model used in this paper has a good effect on rock lithology identification, which proves that the study of this paper has a certain significance.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jinrong Xiao "Lithology identification method of cuttings based on improved VGG16", Proc. SPIE 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023), 127870E (16 August 2023); https://doi.org/10.1117/12.3004563

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KEYWORDS

Data modeling

Education and training

Image segmentation

Feature extraction

Image classification

Performance modeling

Convolution

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