## 5 Conclusions  
In this study, an advanced dual-task processing method based on a CNN is developed for microseismic signals, exhibiting optimized efficiency and accuracy. The method integrates two different networks to solve the onset time picking and detection of micro-seismic waveforms in the time domain. A score map is introduced to label the onset time of micro-fracture waveforms to improve the performance of the developed approach. Various microseismic waveforms recorded from deep-buried tunnel projects, comprising micro-fractures, blasting, mechanical, and unknown noise waveforms, are categorized into two target classes to evaluate the performance of the proposed PDNetwork method, and several statistical parameters are utilized for performance monitoring. The proposed method exhibited superior performance in the onset time picking and detection of microseismic waveforms. The score map of the onset time further helps improve the detection accuracy of microseismic waveforms to meet the engineering requirements in the classification of different types of microseismic waveforms. Compared with the existing methods, this proposed method has a better performance for the onset time picking and detection of microseismic waveforms. The method is proven to have a low sensitivity to noise, even when interfered with by untrained Gaussian noise. In addition, the generalizability of this method is validated using the microseismic data recorded under different geological conditions in other tunnel engineering projects, even when trained on semisynthetic data. Moreover, increasing \(M_{w}\) brought about an improvement in the model performance. While the original objective of this study is to efficiently and automatically process microseismic signals, the proposed methodology can also be applied to signal analysis and disaster estimation in the fields of geophysical and geotechnical engineering. Topics for future research include combining conventional signal processing approaches (such as STA/LTA) with deep learning, using adaptive filters for data preprocessing, and generating the different score maps of onset time picking using different distribution functions for network training. Further, exploring additional model architectures, applying the dual-task approach to other types of signal data, and challenging specific noise conditions or signal complexities can be other directions for future research. Figure 11: Detection results of micro-fracture waveforms under different \(M_{w}\). **Author Contributions:** Writing--original draft preparation, H.Z. ; writing--review and editing, R.L. and C.M. ; investigation, S.M., Z.H., and D.L. ; project administration, X.C. ; funding acquisition, H.Z. and C.M. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the Doctoral Direct Train Project of the Chongqing Natural Science Foundation (CSTB2023 NSCQ-BSX0029); the Natural Science Foundation of China (42477171); Chongqing Technological Innovation and Application Development Project (CSTB2022 TIAD-KPX0144); the Chongqing Construction Science and Technology Plan Project (Chengkezi 2023 No. 1-1); and the Chongqing Natural Science Foundation (2024 NSCQ-MSX4797). **Data Availability Statement:** The raw/processed data required to reproduce these findings cannot be shared at this time as the data also form part of an ongoing study. **Acknowledgments:** The authors would like to thank the editor, assistant editor, and anonymous reviewers for their careful reviews and insightful remarks. **Conflicts of Interest:** Authors [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] were employed by the company Chongqing City Construction Investment (Group) Co., Ltd. Authors [PERSON], [PERSON], were employed by the company Chongqing City Construction Investment Group Wushan Urban Renewal Construction and Development Co., Ltd.