## 1 Introduction  
Microseismic monitoring technology has been widely used in the field of rock mechanics and rockburst prediction. Accurate and efficient detection and onset time picking of micro-fracture signals from massive amounts of recorded data form the basis of microseismic data analysis. A highly accurate onset time picking is used for source location and parameter calculation, as well as disaster prediction [1; 2; 3; 4]. However, 24 h uninterrupted monitoring using microseismic technology produces microseismic records characterized by a large quantity, high complexity, and diversity, particularly in the field of tunnel engineering. The processing of such vast amounts of microseismic data requires significant manpower and time costs. Because of the complexity of the construction environment and geological conditions in tunnel engineering, various types of microseismic records are collected, including blasting, micro-fracture, mechanical, and noise data. Moreover, micro-fracture signals are polluted by noises of different types and intensities, which increase the difficulty of accurate onset time picking. Finally, an inaccurate detection of micro-fracture signals may make the microseismic catalog confusing, and an inaccurate onset time picking will reduce the precision of source location and affect further analysis. The methods used in existing studies to the above problems in the field of seismic/mic/micseismic detection can be mainly divided into two categories: parameter-based and waveform-based methods. Parameter-based methods can detect seismic/microsicnic events using the parameter features, including the radiation pattern, S- to P-wave energy ratio, static stress drop, energy, seismic moment, etc. [5; 6; 7; 8; 9; 10]. Parameter-based methods exhibit a good performance in the detection of seismic/microsicnic events; however, they have a greater dependence on postprocessing by experienced analysts. This increases the burden on manual processing. In comparison, waveform-based methods use only the features of the original waveform [11; 12; 13; 14; 15], thus avoiding the disadvantages of the postprocessing schemes employed in source parameter-based methods. However, unlike seismic records, microseismic records are more complex, bringing challenges to waveform-based methods. In early studies, the onset time picking of waveforms mainly depends on manual recognition. With the improvement in monitoring equipment and an increase in data volumes, early onset time picking no longer meets the requirements of high speed and accuracy. Hence, the onset time picking process has been automated, and some methods have gradually become mainstream, including the short-term average/long-term average (STA/LTA) algorithm [16; 17; 18; 19; 20; 21], Akaike information criterion (AIC) algorithm [22; 23], higher-order statistics [24; 25], and modified energy ratio [26; 27]. Deep learning and data mining algorithms provide opportunities to significantly improve the performance of automated tasks in seismology because they allow more complex reasoning methods to simulate the behavior of human thinking. The convolutional neural network (CNN), which is a typical representative, has excellent performance for both microseismic detection and onset time picking [28; 29; 30; 31; 32]. Moreover, the CNN has superior performance in solving the dual or multiple tasks of seismic/microsicnic signal processing [33; 34; 35], unlike conventional waveform processing methods that generally deal with a single problem. In particular, the onset time picking and detection processes for micro-fracture waveforms are important, as they determine the accuracy of source parameter calculation and the effectiveness of early warning measures for earthquake or rockburst disasters. Hence, it is crucial to improve the accuracy and efficiency of the onset time picking and detection of micro-fracture waveforms from massive amounts of recorded data.