Geological hazards caused by rock failure severely threaten the safety of underground projects, and thus microseismic monitoring systems have been deployed to monitor the rock mass stability. However, due to implicit subseries patterns and sparse distinguishing features, automatic discrimination of the microseismic waveforms of rock fracturing remains a great challenge. Deep neural networks offer powerful learning ability, but the unexplainability of the neural network carries substantial risks to decision-making in safety warning. To this end, we propose an explainable convolutional neural network XTF-CNN that supplies both excellent classification performance and explainability. XTF-CNN consists of two major modules: 1) a dual-channel classification module that learns microseismic features from both the time and frequency domains and 2) an explanation module that demonstrates fine-grained and comprehensible results. Experiments are conducted using microseismic wave-forms collected from a deep tunnel project. The results indicate that XTF-CNN achieves superior classification performance over rival methods and significant comprehensibility.

岩石破坏引起的地质灾害严重威胁着地下工程的安全，因此已经部署了微地震监测系统来监测岩体的稳定性。然而，由于隐含的子系列模式和稀疏的特征，自动识别岩石破裂的微地震波形仍然是一个巨大的挑战。深度神经网络提供了强大的学习能力，但是神经网络的不可解释性给安全预警的决策带来了巨大的风险。为此，我们提出了一种可解释的卷积神经网络XTF-CNN，它提供了出色的分类性能和可解释性。XTF-CNN由两个主要模块组成：1）一个双通道分类模块，可从时域和频域学习微地震特征； 2）一个解释模块，可显示细粒度且可理解的结果。实验是使用从深层隧道工程中收集到的微地震波形进行的。结果表明，XTF-CNN的分类性能优于竞争对手的方法，并且具有明显的可理解性。