Abstract The management of uncertainty in three-dimensional (3D) geologic models has been addressed by researchers across a range of use cases including petroleum and minerals exploration and resource characterization, as well as hydrogeologic, geothermal energy, urban geology, and natural hazard studies. Characterizing uncertainty is a key step toward informed decision-making because knowledge of uncertainty allows the targeted improvement of models, is indispensable to risk analysis, improves reproducibility, and encourages experts to explore alternative scenarios. In the minerals sector there is not a unified approach to uncertainty characterization, nor its mitigation. Assessing and mitigating uncertainty in 3D geologic models is a growing field but quite compartmentalized among different subdisciplines within the geosciences. By comparing uncertainty analysis as implemented for three modeling scenarios: basins, regional hard-rock terranes, and mines; at different stages of their respective workflows, we can better understand what a future “complete” modeling platform could look like as applied to the minerals industry. We analyze uncertainty characterization during the different steps in building 3D models as a generic workflow that consists of (1) geologic and geophysical data acquisition followed by processing and inversion of geophysical data, (2) the interpretation of a number of discrete domains boundaries defined by stratigraphic and structural surfaces, (3) homogeneous or spatially variable properties infilling within each domain, and finally (4) use of the models for downstream predictions based on these properties, such as resulting gravity field, gold grade distribution, fluid flow, or economic potential. Although regional- and mine-scale modelers have much to learn from the basin modeling community in terms of managing uncertainty at different stages of the 3D geologic modeling workflow, perhaps the most important lesson is the need to track uncertainty throughout the entirety of the workflow. At present in the minerals sector, uncertainties have a tendency to be recognized within discrete stages of the workflow but are then forgotten, so that at each stage a “best guess” model is provided for further analysis, and all memory of earlier ambiguity is erased.

Abstract Existing three-dimensional (3-D) geologic systems are well adapted to high data-density environments, such as at the mine scale where abundant drill core exists, or in basins where 3-D seismic provides stratigraphie constraints but are poorly adapted to regional geologic problems. There are three areas where improvements in the 3-D workflow need to be made: (1) the handling of uncertainty, (2) the model-building algorithms themselves, and (3) the interface with geophysical inversion. All 3-D models are underconstrained, and at the regional scale this is especially critical for choosing modeling strategies. The practice of only producing a single model ignores the huge uncertainties that underlie model-building processes, and underpins the difficulty in providing meaningful information to end-users about the inherent risk involved in applying the model to solve geologic problems. Future studies need to recognize this and focus on the characterization of model uncertainty, spatially and in terms of geologic features, and produce plausible model suites, rather than single models with unknown validity. The most promising systems for understanding uncertainty use implicit algorithms because they allow the inclusion of some geologic knowledge, for example, age relationships of faults and onlap-offlap relationships. Unfortunately, existing implicit algorithms belie their origins as basin or mine modeling systems because they lack inclusion of normal structural criteria, such as cleavages, lineations, and recognition of polydeformation, all of which are primary tools for the field geologist that is making geologic maps in structurally complex areas. One area of future research will be to establish generalized structural geologic rules that can be built into the modeling process. Finally, and this probably represents the biggest challenge, there is the need for geologic meaning to be maintained during the model-building processes. Current data flows consist of the construction of complex 3-D geologic models that incorporate geologic and geophysical data as well as the prior experience of the modeler, via their interpretation choices. These inputs are used to create a geometric model, which is then transformed into a petrophysical model prior to geophysical inversion. All of the underlying geologic rules are then ignored during the geophysical inversion process. Examples exist that demonstrate that the loss of geologic meaning between geologic and geophysical modeling can be at least partially overcome by increased use of uncertainty characteristics in the workflow.