We developed a hybrid algorithm using both convolutional and recurrent neural networks (CNNs and RNNs, respectively) to pick phases from archived continuous waveforms in two steps. First, an eight‐layer CNN is trained to detect earthquake events from 30‐second‐long three‐component seismograms. The event seismograms are then sent to a two‐layer bidirectional RNN to pick P‐ and S‐arrival times. The data for training and validation and testing of the networks are obtained from the continuous waveforms of 16 stations recording the aftershock sequence of the 2008 Wenchuan earthquake. The augmented training set has 135,966 PS‐wave arrival‐time pairs. The CNN achieved 94% and 98% hit rate for event and noise segments in the test set, respectively. The RNN picking accuracies for P and S waves are 0.03±0.48 (mean error ± standard deviation) and 0.03±0.56  s, respectively.

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