Towards incorporating uncertainty of structural data in 3D geological inversion

All geological models are subject to several kinds of uncertainty. These can be classified into three different types: data imprecision and quality, inherent randomness and incomplete knowledge. With our approach, we address uncertainties introduced by input data quality in complex three-dimensional (3D) models of subsurface structures (geological models). As input data, we consider parameters of geological structures, i.e. formation and fault boundary points and orientation measurements.Our method consists of five steps: construction of an initial geological model with an implicit potential-field method, assignment of probability distributions to data positions and orientation measurements, simulation of several input data sets, construction of several model realisations based on these simulated data sets and finally the visualisation and analysis of the uncertainties.We test our approach in two generic models, a simple graben setting and a complex dome structure. The first model shows that our approach can evaluate uncertainties from different structures and their interaction. Furthermore, it indicates that the final uncertainty of the model is not simply the sum of all input data uncertainties but complex interactions exist. The second example demonstrates that our approach can handle full three-dimensional settings with overturned surfaces.Results of the uncertainty simulation can be visualised and analysed in several ways, ranging from borehole histograms to uncertainty maps. For complex 3D visualisation and analysis, we use indicator functions. When we apply these, we can treat the visualisation of uncertainties in complex settings in a self-consistent manner.With our simulation method, it is possible to analyse and visualise the uncertainties directly introduced by imprecision in the input data. Our approach is intuitive and straight-forward and suitable in both simple and complex geological settings. It enables detailed insights into the model quality, even for the non-expert. In cases where a geological model is the basis for geophysical simulation, it opens-up the way to geological data-driven ensemble modelling and inversion.