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A Bayesian Deconvolution Approach for Receiver Function Analysis
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DOI：10.1109/TGRS.2010.2050327.png)
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Abstract
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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.
Keywords:
Bayesian inference
deconvolution
expectation-maximization (EM)
Gibbs sampling
inverse-gamma
Monte Carlo methods
receiver function
sparsity
variational Bayes
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