Deep learning and transfer learning of earthquake and quarry-blast
discrimination: Applications to southern California and eastern Kentucky
Abstract
Discrimination between tectonic earthquakes and quarry blasts is
important for accurate earthquake cataloging and seismic hazard
analysis. However, reliable classification is challenging with raw
waveforms and no prior knowledge of source parameters. Here we apply
deep learning to perform this task in southern California and eastern
Kentucky, which differ significantly in available labelled data, class
imbalance and waveform characteristics. Accordingly, we adopt different
strategies for the two regions. First, we directly train a convolutional
neural network (CNN) for southern California due to its data abundancy.
To alleviate class imbalance, the blast data are augmented by randomly
shifting waveform windows. The model for California yields an accuracy
of 91.97% for single-station classification and 97.54% for
network-averaged classification. Second, as eastern Kentucky has a much
smaller data size, we fine-tune the pretrained California model to fit
the Kentucky data. The fine-tuned model yields an accuracy of 97.35%
for single-station classification and 99.46% for network-averaged
classification. The fine-tuned model outperforms the model trained from
scratch. Finally, we use occlusion test and gradient-weighted class
activation mapping to illuminate which parts of waveforms are important
for model prediction. Our results demonstrate that deep learning can
achieve high accuracy in seismic event discrimination with raw waveforms
and that transfer learning is effective and efficient to generalize deep
learning models across different regions.