Bayesian association of multiple infrasound events using long-range propagation models
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The International Monitoring System (IMS) is a worldwide network of monitoring stations that helps to verify compliance with the Comprehensive Nuclear Test-Ban Treaty by detecting events that might indicate violations of the treaty. The IMS uses a combination of four technologies: seismological, radionuclide, hydroacoustic, and infrasound. An important limitation of these technologies is related to the fact that the structure of the propagation medium is partially unknown. This is especially true for the infrasound technology, and indeed, a current trend is to undertake the impact of atmospheric variability on waveforms using computational models. Simulation-based predictions, however, are inherently limited by large uncertainties. Further, many thousands of detections are recorded per week and thus, the problem of calculating plausible waveforms for subsets of detections often leads to computational demands that exceed available resources. In this paper, we present a new powerful statistical model for analyzing and interpreting large-scale IMS data. The method is based on a parallel Markov chain Monte Carlo algorithm and full-wave modeling. The method can detect association when multiple, interacting events are present in the data. The posterior probability of no association can be estimated, thereby providing a way to reduce the false alarm rate in operational-like environments.The International Monitoring System (IMS) is a worldwide network of monitoring stations that helps to verify compliance with the Comprehensive Nuclear Test-Ban Treaty by detecting events that might indicate violations of the treaty. The IMS uses a combination of four technologies: seismological, radionuclide, hydroacoustic, and infrasound. An important limitation of these technologies is related to the fact that the structure of the propagation medium is partially unknown. This is especially true for the infrasound technology, and indeed, a current trend is to undertake the impact of atmospheric variability on waveforms using computational models. Simulation-based predictions, however, are inherently limited by large uncertainties. Further, many thousands of detections are recorded per week and thus, the problem of calculating plausible waveforms for subsets of detections often leads to computational demands that exceed available resources. In this paper, we present a new powerful statistical model for an...