Infrasound propagation through various atmospheric conditions and interaction with environmental factors in- duce highly non-linear and non-stationary effects that make it difficult to extract reliable attributes for classi- fication. We present featureless classification results on the Library of Typical Infrasonic Signals using several deep learning techniques, including long short-term memory, self-normalizing, and fully convolutional neural net- works with statistical analysis to establish significantly superior models. In general, the deep classifiers achieve near-perfect classification accuracies on the four classes of infrasonic events including mountain associated waves, microbaroms, auroral infrasonic waves, and volcanic eruptions. Our results provide evidence that deep neural network architectures be considered the leading candidate for classifying infrasound waveforms which can directly benefit applications that seek to identify infrasonic events such as severe weather forecasting, natural disaster early warning systems, and nuclear weapons monitoring.
Mitchell L. Solomon, Kaylen J. Bryan, Kaleb E. Smith, Dean A. Clauter, Anthony O. Smith, and Adrian M. Peter, "Infrasound threat classification: a statistical comparison of deep learning architectures," Proc. SPIE 10629, Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XIX, 1062917 (Presented at SPIE Defense + Security: April 18, 2018; Published: 16 May 2018); https://doi.org/10.1117/12.2304030.
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