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.