Combining Deep Learning with Physics Based Features in Explosion-Earthquake Discrimination

TL;DR

This paper introduces a hybrid seismic discrimination method combining deep learning on waveforms with physics-based features, improving generalization across regions and providing interpretability insights.

Contribution

It presents a novel dual-branch approach integrating deep learning and physics-based features for earthquake-explosion discrimination, enhancing cross-region performance.

Findings

Hybrid model outperforms single-method models in generalization

Physics-based features contribute significantly to decision accuracy

Grad-CAM visualization reveals waveform regions influencing deep learning decisions

Abstract

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.