Detecting Large Explosions with Machine Learning Models Trained on Synthetic Infrasound Data


Explosions produce low-frequency acoustic (infrasound) waves capable of propagating globally, but the spatio-temporal variability of the atmosphere makes detecting events difficult. Machine learning (ML) is well-suited to identify the subtle and nonlinear patterns in explosion infrasound signals, but a previous lack of ground-truth data inhibited training of generalized models. We introduce a physics-based method that propagates infrasound sources through realistic atmospheres to create 28,000 synthetic events, which are used to train ML classifiers. A simple artificial neural network and modern temporal convolutional network discriminate synthetic events from background noise with >90% accuracy and, more importantly, successfully identify the majority of real-world explosion signals recorded during the Humming Road Runner experiment. ML models trained entirely on physics-based synthetics advance explosion detection capabilities and make ML more viable to related fields lacking training data.


Alex Witsil1, David Fee1, Joshua Dickey2, Raúl Peña2, Roger Waxler3, Philip Blom4

1. Wilson Alaska Technical Center, Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK, USA

2. Air Force Technical Applications Center, Patrick SFB, Melbourne, FL, USA

3. National Center for Physical Acoustics, University of Mississippi, Oxford, MS, USA

4. Los Alamos National Laboratory, Los Alamos, NM, USA

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