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A Bayesian Deconvolution Approach for Receiver Function Analysis

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5 Author(s)
Yildirim, S. ; Dept. of Pure Math. & Math. Stat., Univ. of Cambridge, Cambridge, UK ; Cemgil, A.T. ; Aktar, M. ; Ozakin, Y.
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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.

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Geoscience and Remote Sensing, IEEE Transactions on  (Volume:48 ,  Issue: 12 )