Description: 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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A Bayesian Deconvolution Approach for Receiver Function Analysis.pdf
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