| Citation: | LIU Shanwei，MA Zhiwei，WEI Shiqing，et al. Rock image lithology recognition method based on lightweight convolutional neural network[J]. Bulletin of Geological Science and Technology，2025，${article_volume}（0）：1-11 doi: 10.19509/j.cnki.dzkq.tb20240348
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Lithology identification is a crucial step in the process of oil and gas detection and exploration, providing important guidance for exploration positioning, reservoir evaluation, and the establishment of reservoir models. However, traditional manual lithology identification methods are time-consuming and labor-intensive. Although classical deep learning models achieve high identification accuracy, they often have a large number of parameters. To enhance model accuracy while reducing the number of parameters, the aim of this research is to make the model suitable for real-time lithology identification.
This paper first collected a dataset of
The experimental results show that the RSHFNet model achieved an accuracy of 87.21%, which is a 4.98% improvement over the baseline model. Furthermore, the model's parameters and floating-point operations were reduced to 8.69×106 and 9.3×107, respectively, representing 67% of the model's parameters and 63% of the floating-point operations of the baseline model. This reduction significantly decreases the model's size. Additionally, the RSHFNet model demonstrates superior overall performance compared to existing convolutional neural networks.
The proposed RSHFNet lithology identification model offers high recognition accuracy and strong generalization capabilities while being more lightweight, providing a new approach for real-time lithology identification in the field.
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