An infrasonic event neural network classifier

• Published in: IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339) Vol. 6; pp. 3768 - 3773 vol.6
• Main Authors: Ham, F.M., Leeney, T.A., Canady, H.M., Wheeler, J.C.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 1999
• Subjects: Acoustic signal detection; Acoustic waves; Event detection; Explosions; Frequency; Gravity; Monitoring; Neural networks; System testing; Volcanoes
• ISBN: 0780355296; 9780780355293
• ISSN: 1098-7576; 1558-3902
• DOI: 10.1109/IJCNN.1999.830753

An integral part of the Comprehensive Nuclear Test Ban Treaty International Monitoring System is an infrasonic monitoring network that is capable of detecting and verifying nuclear explosions. Reliable detection of such events must be made from data that may contain other sources of infrasonic phenomena, such as volcano eruptions, mountain associated waves (MAW), gravity waves, and microbaroms, to name a few. Infrasonic waves are sub-audible acoustic waves typically in the frequency range 0.01<f<10 Hz. In the interest of working toward the development of a robust neural network discriminator for the IMS, for defecting and classifying nuclear explosions, we have studied the feasibility of discriminating between the infrasonic signatures of volcano activity (MAW), and internal atmospheric gravity waves using a neural discriminator.