1. A Bayesian Deconvolution Approach for Receiver Function Analysis.
- Author
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Yildirim, Sinan, Cemgil, A. Taylan, Aktar, Mustafa, Ozakin, Yaman, and Ertuzun, Ayşın
- Subjects
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BAYESIAN analysis , *MATHEMATICAL convolutions , *MONTE Carlo method , *RANDOM variables , *MATHEMATICAL models , *DATA analysis , *ALGORITHMS , *EARTHQUAKES - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2010
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