Uncertainty assessment for 3D geologic modeling of fault zones based on geologic inputs and prior knowledge

Abstract

Characterizing the zone of damaged and altered rock surrounding a fault surface is highly relevant to geotechnical and geo-environmental engineering works in the subsurface. Evaluating the uncertainty associated with 3D geologic modeling of these fault zones is made possible using the popular and flexible input-based uncertainty propagation approach to geologic model uncertainty assessment - termed probabilistic geomodeling. To satisfy the automation requirements of probabilistic geomodeling while still preserving the key geometry of fault zones in the subsurface, a clear and straightforward modeling approach is developed based on four geologic inputs used in implicit geologic modeling algorithms (surface trace, structural orientation, vertical termination depth and fault zone thickness). The rationale applied to identifying and characterizing the various sources of uncertainty affecting each input are explored and provided using open-source codes. In considering these sources of uncertainty, a novel model formulation is implemented using prior geologic knowledge (i.e., empirical and theoretical relationships) to parameterize modeling inputs which are typically subjectively interpreted by the modeler (e.g., vertical termination depth of fault zones). Additionally, the application of anisotropic spherical distributions to modeling disparate levels of information available regarding a fault zone's dip azimuth and dip angle is demonstrated, providing improved control over the structural orientation uncertainty envelope. The probabilistic geomodeling approach developed is applied to a simple fault zone geologic model built from historically available geologic mapping data, allowing for a visual comparison of the independent contributions of each modeling input on the combined model uncertainty, revealing that vertical termination depth and structural orientation uncertainty dominate model uncertainty at depth, while surface trace uncertainty dominates model uncertainty near the ground surface. The method is also successfully applied to a more complex fault network model containing intersecting major and minor fault zones. The impacts of the model parameterization choices, the fault zone modeling approach and the effects of fault zone interactions on the final geologic model uncertainty assessment are discussed.