Estimating formation boundaries from 1D inversion of electric and electromagnetic data with multi-layer models can be challenging because of the inevitable regularization of this type of inversion resulting in more or less smeared transition zones between formations. Conventional wisdom has it that inversion with few-layer models will solve the problem by providing models with well defined layer boundaries. However, in modern profile-oriented, laterally correlated inversion, the number of layers is the same for all models along the profile. This may cause lateral formation boundaries to be poorly indicated, and sometimes a specific formation will "change layers" along the profile meaning that layer boundaries are no longer formation boundaries. I suggest a new approach to the definition of formation boundaries. It is based on multi-layer inversion models and finds formation boundaries through a statistical analysis of the set of equivalent models obtained in a stochastic process with a correlation function defined by the posterior covariance matrix of the inversion. The method surmounts several of the difficulties mentioned above. A field example will show a successful application of the method.


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