Journal Article

Global Nuclear Explosion Discrimination Using a Convolutional Neural Network

Williams, Jesse, Barama, Louisa, Newman, Andrew V., Peng, Zhigang

2023

Overview

Using P‐wave seismograms, we trained a seismic source classifier using a Convolutional Neural Network. We trained for three classes: earthquake P‐wave, underground nuclear explosion (UNE) P‐wave, and noise. With the current absence of nuclear testing by countries that have signed the Comprehensive Test Ban Treaty, high quality seismic data from UNEs is limited. Even with limited training data, our model can accurately characterize most events recorded at regional and teleseismic distances, finding over 95% signals in the validation set. We applied the model on holdout datasets of the North Korean test explosions to evaluate the performance on unique region and station‐source pairs, with promising results. Additionally, we tested on the Source Physics Experiment events to investigate the potential for chemical explosions to act as a surrogate for nuclear explosions. We anticipate that machine‐learning models like our classifier system can have broad application for other seismic signals including volcanic and non‐volcanic tremor, anomalous earthquakes, ice‐quakes or landslide‐quakes. Plain Language Summary We train a global seismic event classifier using machine learning on underground nuclear test explosion seismic data. Our classifier model can successfully discriminate (with over 95% accuracy) between underground nuclear explosion, earthquake, and noise signals from stations both regionally and far‐field. Since this model was trained on a relatively small data set (for machine learning applications) we expect that similar methods can be applied to event or discrimination of other unique seismic sources like those from volcanoes, landslides, or glaciers. Key Points We successfully discriminate underground nuclear explosions with a Convolutional Neural network (CNN) trained on P‐wave seismograms Robust global seismic event discrimination is possible with machine learning trained on regional and teleseismic data A CNN trained with historical nuclear explosion data can be applied with high accuracy to other regions, like the six Democratic People's Republic of Korea's test explosions

• Publisher: John Wiley & Sons, Inc,American Geophysical Union (AGU),Wiley
• Subject: Artificial neural networks / Chemical explosions / Classifiers / Earthquakes / Explosions / GEOSCIENCES / Glaciers / global discrimination / Landslides / Learning algorithms / Machine learning / Modelling / Neural networks / Nuclear accidents & safety / nuclear blast / Nuclear explosions / Physics / Seismic activity / Seismic data / Seismograms / Seismological data / seismology / Underground explosions / Uniqueness / Volcanic activity