DocumentCode :
1529431
Title :
A Bayesian Deconvolution Approach for Receiver Function Analysis
Author :
Yildirim, Sinan ; Cemgil, Taylan A. ; Aktar, Mustafa ; Ozakin, Y. ; Ertuzun, Aysin
Author_Institution :
Dept. of Pure Math. & Math. Stat., Univ. of Cambridge, Cambridge, UK
Volume :
48
Issue :
12
fYear :
2010
Firstpage :
4151
Lastpage :
4163
Abstract :
In this paper, we propose a Bayesian methodology for receiver function analysis, a key tool in determining the deep structure of the Earth´s crust. We exploit the assumption of sparsity for receiver functions to develop a Bayesian deconvolution method as an alternative to the widely used iterative deconvolution. We model samples of a sparse signal as i.i.d. Student-t random variables. Gibbs sampling and variational Bayes techniques are investigated for our specific posterior inference problem. We used those techniques within the expectation-maximization (EM) algorithm to estimate our unknown model parameters. The superiority of the Bayesian deconvolution is demonstrated by the experiments on both simulated and real earthquake data.
Keywords :
Bayes methods; deconvolution; earthquakes; expectation-maximisation algorithm; geophysical signal processing; Bayesian deconvolution; Earth crust; Gibbs sampling; earthquake data; expectation-maximization algorithm; iterative deconvolution; posterior inference problem; receiver function analysis; Bayesian methods; Convolution; Costs; Deconvolution; Earthquake engineering; Geophysics computing; Iterative methods; Random variables; Sampling methods; Signal processing algorithms; Bayesian inference; Gibbs sampling; Monte Carlo methods; deconvolution; expectationmaximization (EM); inverse-gamma; receiver function; sparsity; variational Bayes;
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
Publisher :
ieee
ISSN :
0196-2892
Type :
jour
DOI :
10.1109/TGRS.2010.2050327
Filename :
5504200
Link To Document :
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