Interpretable deep learning for roof fall hazard detection in underground mines

Abstract

Roof falls due to geological conditions are major hazards in the mining industry, causing work time loss, injuries, and fatalities. There are roof fall problems caused by high horizontal stress in several large-opening limestone mines in the eastern and midwestern United States. The typical hazard management approach for this type of roof fall hazards relies heavily on visual inspections and expert knowledge. In this context, we proposed a deep learning system for detection of the roof fall hazards caused by high horizontal stress. We used images depicting hazardous and non-hazardous roof conditions to develop a convolutional neural network (CNN) for autonomous detection of hazardous roof conditions. To compensate for limited input data, we utilized a transfer learning approach. In the transfer learning approach, an already-trained network is used as a starting point for classification in a similar domain. Results show that this approach works well for classifying roof conditions as hazardous or safe, achieving a statistical accuracy of 86.4%. This result is also compared with a random forest classifier, and the deep learning approach is more successful at classification of roof conditions. However, accuracy alone is not enough to ensure a reliable hazard management system. System constraints and reliability are improved when the features used by the network are understood. Therefore, we used a deep learning interpretation technique called integrated gradients to identify the important geological features in each image for prediction. The analysis of integrated gradients shows that the system uses the same roof features as the experts do on roof fall hazards detection. The system developed in this paper demonstrates the potential of deep learning in geotechnical hazard management to complement human experts, and likely to become an essential part of autonomous operations in cases where hazard identification heavily depends on expert knowledge. Moreover, deep learning-based systems reduce expert exposure to hazardous conditions.