It is important to autopick an event’s arrival time and classify the corresponding phase for seismic data processing. Traditional arrival-time picking algorithms usually need 3C seismograms to classify event phase. However, a large number of borehole seismic data sets are recorded by arrays of hydrophones or distributed acoustic sensing elements whose sensors are 1C and cannot be analyzed for particle motion or phase polarization. With the development of deep learning techniques, researchers have tried data mining with the convolutional neural network (CNN) for seismic phase autopicking. In the previous work, CNN was applied to process 3C seismograms to detect phase and pick arrivals. We have extended this work to process 1C seismic data and focused on two main points. One is the effect of the label vector on the phase detection performance. The other is to propose an architecture to deal with the challenge from the insufficiency of training data in the coverage of different scenarios of $V_{\mathrm{P}}/V_{\mathrm{S}}$ ratios. Two novel points are summarized after this analysis. First, the width of the label vector can be designed through signal time-frequency analysis. Second, a combination of CNN and recurrent neural network architecture is more suitable for designing a P- and S-phase detector to deal with the challenge from the insufficiency of training data for 1C recordings in time-lapse seismic monitoring. We perform experiments and analysis using synthetic and field time-lapse seismic recordings. The experiments show that it is effective for 1C seismic data processing in time-lapse monitoring surveys.

中文翻译：

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SC-PSNET：一种深度神经网络，使用1C记录进行自动P相和S相检测以及到达时间选择器

重要的是自动选择事件的到达时间并为地震数据处理分类相应的阶段。传统的到达时间选择算法通常需要3C地震图来对事件阶段进行分类。但是，大量水井地震数据集是由水听器阵列或分布式声波传感元件（其传感器为1C）记录的，无法对其进行粒子运动或相位极化分析。随着深度学习技术的发展，研究人员已经尝试使用卷积神经网络（CNN）进行数据挖掘以进行地震相位自动识别。在之前的工作中，CNN被用于处理3C地震图以检测相位和拾取到达。我们将这项工作扩展到处理1C地震数据，并着重于两个要点。一种是标记向量对相位检测性能的影响。$V_{\mathrm{P}}/V_{\mathrm{小号}}$比率。经过分析，总结出两个新颖的观点。首先，可以通过信号时频分析来设计标记向量的宽度。其次，CNN和递归神经网络架构的组合更适合于设计P相和S相检测器，以应对时移地震监测中1C记录的训练数据不足的挑战。我们使用合成和现场延时地震记录进行实验和分析。实验表明，该方法对时移监测中的1C地震数据处理是有效的。