MINIMUM ENTROPY DECONVOLUTION WITH AN EXPONENTIAL TRANSFORMATION

The Minimum Entropy Deconvolution (MED) technique of Wiggins (1977) represents a breakthrough in deconvolution and will undoubtedly find wide application in many fields. MED does not require any phase assumptions about the disturbing function and seeks a deconvolved output which consists of the smallest number of large spikes consistent with the input data. The efficiency of MED is much improved when an exponential transformation is incorporated into the algorithm. This is particularly true when the input traces contain additive noise. In this case the noise suppression characteristics of MED are considerably enhanced by the transformation and the identification of smaller spikes is improved. This paper also presents a kurtosis criterion of simplicity rather than the varimax norm introduced by Wiggins. It appears that for a multiple trace input the kurtosis measure leads to improved results. Copyright © 1979, Wiley Blackwell. All rights reserved