论文标题
将深度学习与基于物理学的特征相结合，爆炸 - 电子歧视
Combining Deep Learning with Physics Based Features in Explosion-Earthquake Discrimination
论文作者
论文摘要
本文将深度学习的力量与基于物理学特征的普遍性相结合，以提出一种地震和爆炸之间地震歧视的先进方法。所提出的方法包含两个分支：直接在地震波形或频谱图上运行的深度学习分支，以及在基于物理的参数特征上运行的第二个分支。这些特征是高频P/S幅度比，以及局部幅度（ML）和CODA持续时间幅度（MC）之间的差异。当应用于新区域时，该组合可以比仅使用深度学习开发的模型实现更好的概括性能。我们还检查了波形数据的哪些部分主导着深度学习决策（即通过Grad-CAM）。这种可视化为机器学习模型的黑框性质提供了一个窗口，并为深度学习派生模型如何使用数据做出决策提供了新的见解。
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.
