Abstract: Inversion algorithms of Magnetotelluric (MT) data usually involve a systematic search for the earth model which best fits the observed data. The inversion proceeds by minimizing an objective functional which includes the difference between the observed and the predicted data and a regularization function. The regularization term expresses the prior assumptions about the geology, and allows to reduce the presence of artifacts in the conductivity models reconstructed from sparse, noisy MT data sets. In the classical regularization method a minimum structure models are obtained with the help of a ``smoothing'' operator which essentially performs a numerical first or second derivative on the conductivies, and explicitly suppress complexity from the inverse solutions. Recently, a new regularization approach has been proposed by Ramos and Campos Velho (1996) based on the minimization of the entropy measure of the vector of {\it first-differences} of the unknown parameters. While the existing regularization schemes, such as maximum entropy or Occam's inversion, search for ``the smoothest model which fits the data to within an expected tolerance'', this new approach looks for locally smooth regions separated by sharp discontinuities. Any reconstruction sharing these features has a high level of information and thus a low entropy content. Many geophysically interesting properties and structures may behave in a similar fashion.