Bayesian Deconvolution of Signals Observed on Arrays

Ming Lin, Eric A. Suess, Robert H. Shumway, Rong Chen

Research output: Contribution to journalArticlepeer-review


Time series data collected from arrays of seismometers are traditionally used to solve the core problems of detecting and estimating the waveform of a nuclear explosion or earthquake signal that propagates across the array. We consider here a parametric exponentially modulated autoregressive model. The signal is assumed to be convolved with random amplitudes following a Bernoulli normal mixture. It is shown to be potentially superior to the usual combination of narrow band filtering and beam forming. The approach is applied to analyzing series observed from an earthquake from Yunnan Province in China received by a seismic array in Kazakhstan.

Original languageEnglish (US)
Pages (from-to)837-850
Number of pages14
JournalJournal of Time Series Analysis
Issue number6
StatePublished - Nov 1 2016

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


  • Nonlinear models, Bayes, Markov chain Monte Carlo, deconvolution, seismic arrays, nuclear monitoring


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