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Bayesian Deconvolution of Signals Observed on Arrays
Author(s) -
Lin Ming,
Suess Eric A.,
Shumway Robert H.,
Chen Rong
Publication year - 2016
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/jtsa.12197
Subject(s) - deconvolution , seismometer , waveform , series (stratigraphy) , seismic array , autoregressive model , mathematics , amplitude , signal (programming language) , bayesian probability , algorithm , wavelet , parametric statistics , statistics , seismology , geology , computer science , physics , artificial intelligence , telecommunications , optics , paleontology , radar , programming language
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.