Combining Deep Learning with Physics Based Features in Explosion-Earthquake Discrimination
This work addresses seismic discrimination for monitoring and safety applications, but it is incremental as it builds on existing methods by integrating physics-based features.
The paper tackles the problem of discriminating between earthquakes and explosions using seismic data by combining deep learning with physics-based features, achieving better generalization to new regions than deep learning alone.
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.