A two-dimensional approach to quantify stratigraphic uncertainty from borehole data using non-homogeneous random fields

In many instances, conditions in the subsurface are highly variable, while site investigations only provide sparse measurements. Consequently, subsurface models are usually inaccurate. These characteristics reflect uncertainty and mean significant engineering and environmental hazards. Such uncertainty should be quantified, in order, ultimately, to be reduced. To this end, in this paper, a new two-dimensional approach to quantify stratigraphic uncertainty is proposed and described. The approach is based on non-homogeneous random fields and considers categorical quantities to represent geological structures. Unlike other reported approaches in related literature, we provide evidence on the usefulness of the approach to: (i) comprehensively explore the many diverse and potential geological structures' configurations to exhaust uncertainty quantification; (ii) facilitate the representation of non-homogeneous fields in a computational inexpensive fashion; and (iii) ameliorate the complication of producing spatial Markov chains or Bayesian models to quantify uncertainty using finite difference equations. The proposed approach is demonstrated using a case analysis.