Nuclear proliferationSoftware helps detect nuclear tests
When North Korea conducted its recent nuclear weapon test, it was not terribly difficult to detect. It was a fairly large blast, it occurred in a place where a test was not surprising, and the North Korean government made no effort to hide it. But clandestine tests of smaller devices, perhaps by terrorist organizations or other nonstate actors, are a different story. It is those difficult-to-detect events that the Vertically Integrated Seismic Analysis (VISA) — a machine learning system — aims to find.
When North Korea conducted its recent nuclear weapon test, the blast had been detected by a global seismic sensing network operated by the Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO). The network, called the International Monitoring System, aims to “make sure that no nuclear explosion goes undetected.” Software designed in part by a Brown University computer scientist is helping to do just that.
The most recent North Korean test was not terribly difficult to detect. It was a fairly large blast, it occurred in a place where a test was not surprising, and the North Korean government made no effort to hide it. But clandestine tests of smaller devices, perhaps by terrorist organizations or other nonstate actors, are a different story. Brown University reports that it is those difficult-to-detect events that VISA — a machine learning system that Brown University’s Erik Sudderth helped to design — aims to find.
The International Monitoring System includes 149 certified seismic monitoring stations around the globe. Those stations send data to the CTBTO’s Vienna headquarters, where analysts compile all seismic events into a daily bulletin supplied to nations around the world. The vast majority of events detected by the system are natural — earthquakes and seismic tremors of various sorts. But occasionally, like recently in North Korea, an event is triggered by a large explosion.
Analysts can easily pick out unnatural events from the characteristics of the seismic waveforms they create, but before they can determine whether an event is unnatural, they need to know that an event has occurred.
“You have hundreds of stations all over the world producing high-dimensional data that’s streaming in 24-by-seven,” said Sudderth, assistant professor of computer science. “[People] can’t look at all the data all the time. They need the help of automated tools.”
Those automated tools keep a constant eye on every station and create a log of potential local detections. They also combine data from multiple stations to hypothesize the time, location, and magnitude of plausible seismic events. Analysts then look at those data to determine if indeed each detection was from a seismic event or just represents random noise. Once an event is confirmed to be real, analysts review it to determine whether it was natural or human-made.
