We propose a frequency blind deconvolution algorithmbased on mutual information rate as a measure of whiteness. In the case of seismic data, the algorithm ofWiggins [11] based on kurtosis, which is a supergaussianity criterion, is often used. We study the robustness in noisy context of these two algorithms, and compare them with Wiener filtering. We provide some theoretical explanations on the effect of the additive noise. The theoretical arguments are illustrated with a simulation of seismic signals. For such signal, the supergaussianity criterion appears more robust to noise contamination than the whiteness criterion.
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