Detecting Large Explosions With Machine Learning Models Trained on Synthetic Infrasound Data
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 04, 2022
- Source ID
- 10.1029/2022gl097785
Entities
People
- Alex J. C. Witsil
- David Fee
- Joshua Dickey
- Philip Blom
- Raúl Peña
- Roger Waxler
Organizations
- Air Force Technical Applications Center
- Defense Threat Reduction Agency
- Los Alamos National Laboratory
- University of Alaska System
- University of Mississippi