The availability of abundant digital seismic records and successful application of deep learning in pattern recognition and classification problems enable us to achieve a reliable earthquake detection framework. To overcome the limitations and challenges of conventional methods, which are mainly due to an incomplete set of template waveforms and low signal‐to‐noise ratio, we design a generalized model to improve discrimination between earthquake and noise recordings using a deep convolutional network (ConvNet). Exclusively based on a dataset of over 4900 earthquakes recorded over a period of 3 yrs in western Canada, a multilayer ConvNet is trained to learn general characteristics of background noise and earthquake signals in the time–frequency domain. In the next step, we train a secondary network using the wavelet transform of the major seismic arrivals to separate P from S waves and estimate their approximate arrival times. The results of validation experiments demonstrate promising performance and achieve an average accuracy of nearly 99% for both networks. To investigate the applicability of our algorithm, we apply the trained model on an independent dataset recently recorded in northeastern British Columbia (NE BC). It is found that deep‐learning‐based methods are superior to traditional techniques in detecting a higher number of seismic events at significantly less computational cost.