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Global Nuclear Blast Discrimination using a Convolutional Neural Network
  • +1
  • Louisa Barama,
  • Jesse Williams,
  • Andrew V Newman,
  • Zhigang Peng
Louisa Barama
Georgia Institute of Technology

Corresponding Author:[email protected]

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Jesse Williams
Global Technology Connection
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Andrew V Newman
Georgia Institute of Technology
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Zhigang Peng
Georgia Institute of Technology
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Abstract

Using P-wave seismograms by Barama et al. (2022), we trained a seismic source classifier using a Convolutional Neural Network. We trained for three classes: earthquake P-wave, nuclear P-wave, and noise. Seismograms with low signal to noise ratios (SNR) adversely affect the model performance, thus a threshold was applied, limiting the training set size. Our method can accurately characterize most events, finding over 95\% signals in the validation set, even with the SNR-limited training data. We applied the model on holdout datasets of the North Korean test blasts to evaluate the performance on unique region and station-source pairs. Additionally, we tested on the Source Physics Experiment events to investigate the potential for chemical blasts to act as a surrogate for nuclear blasts. 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.