Entropy guided deconvolution

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Abstract
The day‐to‐day application of minimum entropy deconvolution has rarely lived up to expectations based on a few of the first selected examples. This appears to be the result of (a) the multimaxima nature of the varimax function or other similar nonlinear norms, and (b) the excessive sensitivity of the estimated filter to the probability distribution of the underlying random series. As the distribution approaches Gaussian, the variance of the filter coefficients becomes unbounded. The alternative strategy proposed consists of solving for the inverse operator as a sequence of two‐term operators each having an independently determined lag. The amplitude and sign of the operator coefficient are estimated by least squares; the discrimination between minimum or maximum phase is determined by the varimax criterion.

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