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Deep learning based lithology classification of drill core images.

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This study introduces an automated method for identifying drill core lithology using a convolutional neural network (CNN). The ResNeSt-50 model achieved high accuracy, significantly improving efficiency in geological analysis.

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Area of Science:

  • Geology
  • Computer Science
  • Artificial Intelligence

Background:

  • Drill core lithology is crucial for understanding geological conditions.
  • Manual lithology identification is labor-intensive and requires expertise.
  • Convolutional neural networks (CNNs) offer potential for automated image analysis.

Purpose of the Study:

  • To develop an automated system for drill core lithology classification.
  • To evaluate the performance of a ResNeSt-50 CNN model for this task.

Main Methods:

  • A dataset of 10 common lithology categories from underground engineering was created.
  • The ResNeSt-50 model, incorporating channel-wise attention and multi-path networks, was employed.
  • Transfer learning was utilized to optimize feature extraction from core images.

Main Results:

  • The ResNeSt-50 model demonstrated superior performance compared to other CNN models.
  • The automated classification achieved an average Precision, Recall, and F1-score of 99.62%, 99.62%, and 99.59%, respectively.
  • The overall prediction accuracy reached 99.60%.

Conclusions:

  • The proposed CNN-based method is highly effective for automatic lithology classification of borehole cores.
  • This approach offers a significant improvement over traditional manual identification methods.
  • The study validates the potential of advanced deep learning techniques in geological applications.