Reliable automatic microseismic waveform detection with high efficiency, precision, and adaptability is the basis of stability analysis of the surrounding rock mass. In this paper, a convolutional neural network (CNN)-based microseismic detection network (CNN-MDN) model was established and well trained to a high degree of accuracy using a dataset with 16,000 preprocessed waveforms. By comparison with other methods, 4000 waveforms were tested to evaluate the precision, recall, and F1-score. The results revealed that the CNN-MDN demonstrated the highest performance in microseismic detection. Moreover, the low sensitivity of the CNN-MDN to noise of different intensities was proved by testing on semi-synthetic data. The model also possesses good generalization ability and superior performance capability for microseismic detection under different geological structure backgrounds, and it can correctly detect the microseismic events with Mw ≥ 0.5. These preliminary results show that the CNN-MDN can be directly applied to unprocessed microseismic data and has great potential in real-time microseismic monitoring applications.

中文翻译：

---

用于微地震信号检测和分类的深度卷积神经网络

可靠、高效、精确、适应性强的自动微震波形检测是围岩稳定性分析的基础。在本文中，建立了基于卷积神经网络 (CNN) 的微地震检测网络 (CNN-MDN) 模型，并使用具有 16,000 个预处理波形的数据集进行了高精度训练。通过与其他方法的比较，测试了4000个波形来评估准确率、召回率和F1-score。结果表明，CNN-MDN 在微震检测中表现出最高的性能。此外，通过对半合成数据的测试，证明了 CNN-MDN 对不同强度噪声的敏感性较低。该模型对不同地质构造背景下的微地震检测也具有良好的泛化能力和优越的性能，能够正确检测Mw≥0.5的微地震事件。这些初步结果表明，CNN-MDN 可以直接应用于未经处理的微地震数据，在实时微地震监测应用中具有巨大潜力。