Global Seismic Monitoring: A Bayesian Approach

Nimar S. Arora, Stuart Russell, Paul Kidwell, Erik B. Sudderth

AAAI 2011 pp. 1533-1536

doi:10.1609/AAAI.V25I1.7955 /aaai/2011/arora2011aaai-global/

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. 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 50% in the number of missed events compared to the currently deployed system. It also finds events that are missed even by the human analysts who post-process the IMS output.

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Text

Arora et al. "Global Seismic Monitoring: A Bayesian Approach." AAAI Conference on Artificial Intelligence, 2011. doi:10.1609/AAAI.V25I1.7955

Markdown

[Arora et al. "Global Seismic Monitoring: A Bayesian Approach." AAAI Conference on Artificial Intelligence, 2011.](https://mlanthology.org/aaai/2011/arora2011aaai-global/) doi:10.1609/AAAI.V25I1.7955

BibTeX

@inproceedings{arora2011aaai-global,
  title     = {{Global Seismic Monitoring: A Bayesian Approach}},
  author    = {Arora, Nimar S. and Russell, Stuart and Kidwell, Paul and Sudderth, Erik B.},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2011},
  pages     = {1533-1536},
  doi       = {10.1609/AAAI.V25I1.7955},
  url       = {https://mlanthology.org/aaai/2011/arora2011aaai-global/}
}