LITHOLOGY IDENTIFICATION OF GEOLOGICAL IMAGES BASED ON DEEP LEARNING AND CLASS IMBALANCE PROCESSING

The integration of artificial intelligence and geological sciences has accelerated the digital transformation of geotechnical engineering, with lithology identification emerging as a pivotal task in geological exploration and tunnel construction. While automated lithology recognition offers substantial advantages in operational safety and cost efficiency, two critical challenges persist: (1)the scarcity of domain-specific geological image datasets from real tunnel environments, and (2) the adverse impact of severe class imbalance on minority category recognition accuracy. To address these dual challenges, our study developed a tunnel construction-specific geological image dataset through systematic collection and preprocessing, followed by a comprehensive evaluation of class imbalance mitigation strategies. A residual neural network architecture enhanced by a weight-adaptive Softmax loss function was proposed, which dynamically calibrates category weights based on sample distribution to suppress majority-class dominance during training. The framework synergizes ResNet's hierarchical feature extraction with an attention-driven imbalance correction mechanism, achieving 95% overall accuracy in lithology identification. Attention heatmap visualization further confirmed the model's alignment with expert-interpretable geological features, demonstrating both favorable technical efficacy and engineering-oriented interpretability.