【Objective】Lithology identification is a crucial step in the process of oil and gas exploration and development, 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 is to make the model suitable for real-time lithology identification.【Methods】This paper first collected a dataset of 3,016 rock images consisting of eight types, including dolomite and sandstone. Based on the lightweight convolutional neural network ShuffleNetV2, the paper proposes a Rock-ShuffleNetV2 lithology identification model (hereafter referred to as the RSHFNet model). The model incorporates the Convolutional Block Attention Module (CBAM) and Multi-Scale Feature Fusion Module (MSF) into the base network to enhance feature extraction capabilities and improve identification performance. Additionally, the number of stacked ShuffleNetV2 units is optimized to reduce the model's parameters.【Results】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 × 10^6 and 9.3 × 10^7, respectively, representing 67% and 63% 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.【Conclusion】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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