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.