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Peer-reviewedCC BY 4.0PLOS One

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

  • Yan Zhang1,2
  • Fan Zhang1,2
  • Jiaming Li1,2
  • Lantao Huang1,2
  • Ruifeng Li1,2
  1. 1Guangxi Key Laboratory of Geomechanics and Geotechnical Engineering, Guilin University of Technology, Guilin, China
  2. 2Guangxi Key Laboratory of Green Building Materials and Construction Industrialization, Guilin University of Technology, Guilin, China
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Abstract

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.

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Cite

BibTeXRIS
BibTeX
@article{zhang2026multiattention,
  title = {{A multi-attention mechanism-improved ResNet18 approach for lithology identification and classification}},
  author = {Yan Zhang and Fan Zhang and Jiaming Li and Lantao Huang and Ruifeng Li},
  journal = {PLOS One},
  year = {2026},
  month = oct,
  doi = {10.1371/journal.pone.0361625},
  url = {https://doi.org/10.1371/journal.pone.0361625},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - A multi-attention mechanism-improved ResNet18 approach for lithology identification and classification
AU  - Yan Zhang
AU  - Fan Zhang
AU  - Jiaming Li
AU  - Lantao Huang
AU  - Ruifeng Li
PY  - 2026
DA  - 2026/10/27
JO  - PLOS One
DO  - 10.1371/journal.pone.0361625
UR  - https://doi.org/10.1371/journal.pone.0361625
AB  - 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.
SN  - 1932-6203
ER  -