Classification of Infrasonic Atmospheric Events Using Electromagnetic Pulse Analysis. Issue 5 (2nd March 2022) - Kahlon, Maayan; Lipshtat, Azi; Price, Colin; Weiss, Anthony
Abstract: The Comprehensive Nuclear‐Test‐Ban Treaty Organization (CTBTO) operates the international monitoring system (IMS), consisting of four complementary technologies: Seismic, hydroacoustic, infrasound, and radionuclide, to detect events that might indicate a treaty violation. Infrasound is the main technology aimed at detecting atmospheric nuclear explosions. However, there are many other sources of infrasound signals, which are low frequency inaudible sound‐waves in the atmosphere, that are detected. To deny that the source is of a nuclear nature, requires a lot of resources. As opposed to most other sources of infrasound, nuclear explosions also emit an electromagnetic pulse (EMP). Thus, if an infrasonically detected event is not accompanied by an EMP, it is certain that it has not originated from a nuclear explosion. In this research we use electromagnetic data recorded by a single antenna to examine coincidence of infrasonically detected events with detected EMPs. We show that a large fraction of infrasonically detected events are not accompanied by any EMP and thus the need to analyze them in the context of the IMS is eliminated. For those infrasonically detected events that do coincide with EMPs, we use machine learning techniques to classify the EMPs as either lightning or a potential explosion. We show that one can identify the lightning out of the EMPs. By filtering out all lightning signals one can almost always verify that there is no EMP other than lightning, which coincided with the infrasonically detected event. This way we can dramatically reduce the number of infrasound signals which require manual analysis. Plain Language Summary: Nuclear explosions, as opposed to other sources of infrasound signals, emit a noticeable electromagnetic pulse. We suggest to use the existence or absence of such a pulse as a first indicator to the possibility that a given infrasound event resulted from a nuclear explosion. Lightning discharge might also coincide with the infrasound detected event. We show how to filter out the lightning signals, and distinguish between lightning and explosions using simple machine learning methods. Key Points: Atmospheric nuclear explosions emit both electromagnetic pulses and infrasound signals, that may be used for monitoring nuclear explosions Many infrasonicly detected events coincide with lightning discharges and may be considered erroneously as nuclear explosions Lightning discharges can be discriminated from nuclear electromagnetic pulses by means of machine learning using their different properties