التفاصيل البيبلوغرافية
| العنوان: |
Combining Deep Learning With Physics Based Features in Explosion‐Earthquake Discrimination. |
| المؤلفون: |
Kong, Qingkai, Wang, Ruijia, Walter, William R., Pyle, Moira, Koper, Keith, Schmandt, Brandon |
| المصدر: |
Geophysical Research Letters; 7/16/2022, Vol. 49 Issue 13, p1-10, 10p |
| مصطلحات موضوعية: |
DEEP learning, EXPLOSIONS, ELASTIC wave propagation, MACHINE learning, SHEAR waves, PHYSICS |
| مستخلص: |
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 decisions. Plain Language Summary: This paper presents a new method to distinguish earthquakes from explosions using seismic data. The method combines features implicitly defined by a deep learning algorithm with features explicitly defined from physical models of seismic sources and elastic wave propagation. The combination of these two types of features makes our method perform better on new data sets. By visualizing the performance of our combined model, we gain insight into what the deep learning model relies on to make its decisions. Key Points: Discrimination of earthquakes and explosions can be enhanced by combining physics‐based features with those derived from machine learningVisualizing which parts of the input the deep learning model relies on can provide more insight into the processes underlying decisionsThe deep learning model focuses on different frequency bands for P and S waves to make the decision [ABSTRACT FROM AUTHOR] |
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