  This paper combines the power of deep-learning with the generalizability of
physics-based features, to present an advanced method for seismic
discrimination between earthquakes and explosions. The proposed method contains
two branches: a deep learning branch operating directly on seismic waveforms or
spectrograms, and a second branch operating on physics-based parametric
features. These features are high-frequency P/S amplitude ratios and the
difference between local magnitude (ML) and coda duration magnitude (MC). The
combination achieves better generalization performance when applied to new
regions than models that are developed solely with deep learning. We also
examined which parts of the waveform data dominate deep learning decisions
(i.e., via Grad-CAM). Such visualization provides a window into the black-box
nature of the machine-learning models and offers new insight into how the deep
learning derived models use data to make the decisions.
