The deep learning-based method demonstrates superior capability in high-temperature deterioration analysis of rock materials through scanning electron microscopy (SEM) characterization. However, the blurred boundaries between rock particles and pore structure presented in SEM images always affect the training efficiency. Hence, in this study, the metal intrusion technology and backscattered electron (BSE) observations are applied to assist a deep learning-based model to analyze the deterioration of the rock materials exposed to different levels of high temperatures. >2000 micro images for each level were inputted and trained to distinguish the features of rock after 5 temperature levels deterioration. The results reveal that the classification accuracy on the heat deterioration of the rock materials achieved 94.3%. The classification accuracy under the optimized observation area (from 240 pixels × 240 pixels to 280 pixels × 280 pixels) is stable over 90%, balancing the high precision and training efficiency. Moreover, the interpretable high-temperature degradation characteristics of the micro-damage on rock particles and pore regions can be extracted, enabling further analysis of the degradation process of high-temperature treated rocks. We expect this work will inspire a high-precision, wide-application and cost-efficient method for the deterioration analysis of rock materials in underground projects.

中文翻译：

---

基于金属侵入增强深度学习的岩石材料高温劣化分析

基于深度学习的方法通过扫描电子显微镜（SEM）表征展示了岩石材料高温劣化分析的卓越能力。然而，SEM图像中呈现的岩石颗粒和孔隙结构之间的模糊边界总是影响训练效率。因此，在这项研究中，应用金属侵入技术和背散射电子（BSE）观测来辅助基于深度学习的模型来分析暴露在不同高温水平下的岩石材料的劣化。每个级别输入超过2000张微图像并进行训练，以区分5个温度级别恶化后的岩石特征。结果表明，岩石材料热劣化分类准确率达到94.3%。优化观察区域（从240像素×240像素到280像素×280像素）下的分类精度稳定在90%以上，平衡了高精度和训练效率。此外，还可以提取岩石颗粒和孔隙区域微损伤的可解释高温降解特征，从而能够进一步分析高温处理岩石的降解过程。我们期望这项工作将为地下工程中岩石材料的劣化分析提供一种高精度、广泛应用且经济高效的方法。