A multi-attention mechanism-improved ResNet18 approach for lithology identification and classification

Lithology identification is vital for improving geological exploration accuracy, optimizing petroleum exploration schemes, and ensuring construction engineering safety. Traditional identification methods suffer from low efficiency, high cost, and heavy dependence on domain expertise. This study proposes a lithology identification approach based on the ResNet18 backbone, in which five lightweight attention mechanisms, namely CBAM, channel-only attention, ECA, SE, and spatial-only attention, are systematically compared, and the best-performing CBAM module is embedded to strengthen feature extraction and discrimination capacity. The Open-Source Computer Vision (OpenCV) library is adopted for dataset preprocessing, where raw images are converted to grayscale to suppress interference from irrelevant information. Comparative experiments demonstrate that CBAM outperforms the other evaluated attention modules and is therefore incorporated into the final network architecture. A hyperparameter sensitivity analysis of the CBAM-based model demonstrates that the model performance varies with different combinations of learning rate and batch size. When trained with a learning rate of 0.0001 and a batch size of 16, the model achieves a lithology identification accuracy of 86.36%, substantially improving overall recognition performance. By integrating CBAM into ResNet18, the proposed method enables accurate recognition to support multi-field engineering applications, reduce risks, and promote intelligent precision.