Identifying useful microseismic events is one of the key steps in monitoring tunnel rockbursts. Here, we propose a strong noise-tolerance deep learning (SNTDL) network for the automatic classification of noisy microseismic events. The training set, validation set, and test set of the SNTDL network consist of 27989 unfiltered microseismic recordings. First, to comprehensively characterize the microseismic events, we extract ten weakly correlated features of the microseismic recordings as the input of the SNTDL network. Then, the skip connection and concatenation structure are added to this network, which can further enhances its generalization ability. Additionally, the SNTDL, AlexNet, Inception, visual geometry group, and ResNet are compared using the synthetic microseismic recordings with different signal-noise ratios. The results demonstrate that the SNTDL network has a higher accuracy and stronger noise-tolerance capability than the other approaches. Application to a dataset collected from a different construction environment confirms that the SNTDL network can still achieve an accurate classification result, which further verifies that the proposed network has a reliable generalization performance.

中文翻译：

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用于自动微震事件分类的强抗噪深度学习网络

识别有用的微震事件是监测隧道岩爆的关键步骤之一。在这里，我们提出了一个强大的抗噪深度学习 (SNTDL) 网络，用于对嘈杂的微震事件进行自动分类。SNTDL 网络的训练集、验证集和测试集由 27989 个未过滤的微震记录组成。首先，为了全面描述微震事件，我们提取了微震记录的十个弱相关特征作为 SNTDL 网络的输入。然后，在这个网络中加入skip connection和concatenation结构，可以进一步增强其泛化能力。此外，使用具有不同信噪比的合成微震记录比较了 SNTDL、AlexNet、Inception、视觉几何组和 ResNet。结果表明，与其他方法相比，SNTDL 网络具有更高的准确性和更强的噪声容忍能力。应用于从不同构建环境收集的数据集证实了 SNTDL 网络仍然可以达到准确的分类结果，这进一步验证了所提出的网络具有可靠的泛化性能。