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Bayesian monitoring for the Comprehensive Nuclear-Test-Ban Treaty

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If you have a question about this talk, please contact Zoubin Ghahramani .

Verification for the Comprehensive Nuclear-Test-Ban Treaty requires detecting and characterizing all seismic events above a minimum magnitude occurring anywhere on Earth. The treaty defines a network of sensors, the International Monitoring System (IMS), managed by the United Nations CTBTO . NET-VISA, a Bayesian monitoring system applied to IMS data, exhibits a 2x-3x reduction in detection failures compared to the current CTBTO system; the UN has recommended its deployment for treaty verification, subject to approval by member states.

NET -VISA’s prior is a complex, open-universe generative probability model (written originally in the Bayesian Logic formal language and trained on historical data) describing event occurrence, signal propagation, signal detection, and noise processes; the evidence consists of “blips” (above-threshold signals, 90% of which are noise) extracted from raw IMS waveform data. More recent work extends the generative model all the way to the raw waveforms, promising greater sensitivity but requiring new modeling and inference techniques.

Joint work with Nimar Arora, Erik Sudderth, and David Moore

This talk is part of the Machine Learning @ CUED series.

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