Model-based Bayesian Seismic Monitoring

This thesis presents the underlying probabilistic model, the parameterestimation, and the inference algorithm of NET-VISA, NetworkProcessing Vertically Integrated Seismic Analysis. NET-VISA is an OpenUniverse Probability Model (OUPM) for seismic events, the transmissionof seismic waves through the earth, and their detection (or misdetection) at stations, as well as a model for spurious detections.The probabilistic model allows for seamless integration of various disparate sources of information. Applied in the context of the International Monitoring System (IMS), a global sensor network developed for the Comprehensive Nuclear-Test-Ban Treaty (CTBT), NET-VISA achieves a reduction of around 60% in the number of missed events compared to the currently deployed system. It also finds events that are missed by the human analysts who post-process the IMS output

The Comprehensive Nuclear Test Ban Treaty (CTBT),

Abstract The automated processing of multiple seismic signals to detect and localize seismic events is a central tool in both geophysics and nuclear treaty verification. This paper reports on a project begun in 2009 to reformulate this problem in a Bayesian framework. A Bayesian seismic monitoring system, NET-VISA, has been built comprising a spatial event prior and generative models of event transmission and detection, as well as an inference algorithm. The probabilistic model allows for seamless integration of various disparate sources of information. Applied in the context of the International Monitoring System (IMS), a global sensor network developed for the Comprehensive Nuclear Test Ban Treaty (CTBT), NET-VISA achieves a reduction of around 60 % in the number of missed events compared with the currently deployed system. It also finds events that are missed by the human analysts who postprocess the IMS output