Global Nuclear Explosion Discrimination Using a Convolutional Neural Network
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 08, 2023
- Source ID
- 10.1029/2022gl101528
Entities
People
- A. V. Newman
- Jesse Williams
- Louisa Barama
- Zhigang Peng
Organizations
- Air Force Research Laboratory
- Georgia Tech
- Lawrence Livermore National Laboratory