Infrasound threat classification: a statistical comparison of deep learning architectures

Mitchell L. Solomon; Kaylen J. Bryan; Kaleb E. Smith; Dean A. Clauter; Anthony O. Smith; Adrian M. Peter

doi:10.1117/12.2304030

16 May 2018 Infrasound threat classification: a statistical comparison of deep learning architectures

Mitchell L. Solomon, Kaylen J. Bryan, Kaleb E. Smith, Dean A. Clauter, Anthony O. Smith, Adrian M. Peter

Proceedings Volume 10629, Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XIX; 1062917 (2018) https://doi.org/10.1117/12.2304030
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

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.

Conference Presentation

Citation Download Citation

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 (16 May 2018); https://doi.org/10.1117/12.2304030

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available