Explainable time-frequency convolutional neural network for microseismic waveform classification
Explainable time-frequency convolutional neural network for microseismic waveform classification
复制标题
用于微震波形分类的可解释时频卷积神经网络
DOI：
10.1016/j.ins.2020.08.109
复制
发表时间：
2021-02-06
影响因子：
8.1
通讯作者：
Ma, Yuliang
中科院分区：
文献类型：
--
作者：
Bi, Xin;Zhang, Chao;Ma, Yuliang
岩石破坏引起的地质灾害严重威胁地下工程的安全，因此需要部署微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区分特征，岩石破裂微震波形的自动判别仍然是一个巨大的挑战。深度神经网络具有强大的学习能力，但神经网络的不可解释性给安全预警决策带来了巨大风险。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）双通道分类模块，从时域和频域学习微震特征；2）解释模块，展示细粒度和可理解的结果。使用从深层隧道项目收集的微震波形进行实验。结果表明，XTF-CNN 实现了优于竞争对手方法的分类性能和显着的可理解性。 (c) 2020 Elsevier Inc. 保留所有权利。
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. (c) 2020 Elsevier Inc. All rights reserved.