In anticipation to substitute the existing manual/semi-automated methods for classifying quarry blasts, earthquakes, and noise, we developed three convolutional neural network (CNN) models. The three CNN models extract relevant features from seismograms (waveform), spectrograms (spectrum), and a combination of the two respectively. A total of 3414 samples were extracted from the three categories, 15% of the data from each category were split for testing, and the remaining data were augmented and used for training. The waveform model, spectrogram model, and combined model achieved accuracies of 95.32%, 93.13%, and 93.96%, respectively. The reliability of these models was ascertained by promising accuracies of >90% and 100% obtained for large and small datasets from testing with SCEDC data and records from the Palitana region (Gujarat) respectively. The results of this study demonstrate the potential of deep learning-based approaches for the effective classification of seismic events.