Abstract
Abstract. Exhumed basement rocks are often dissected by faults, the latter controlling physical parameters such as rock strength, porosity, or permeability. Knowledge on the three-dimensional (3-D) geometry of the fault pattern and its continuation with depth is therefore of paramount importance for applied geology projects (e.g. tunnelling, nuclear waste disposal) in crystalline bedrock. The central Aar massif (Central Switzerland) serves as a study area where we investigate the 3-D geometry of the Alpine fault pattern by means of both surface (fieldwork and remote sensing) and underground ground (mapping of the Grimsel Test Site) information. The fault zone pattern consists of planar steep major faults (kilometre scale) interconnected with secondary relay faults (hectometre scale). Starting with surface data, we present a workflow for structural 3-D modelling of the primary faults based on a comparison of three extrapolation approaches based on (a) field data, (b) Delaunay triangulation, and (c) a best-fitting moment of inertia analysis. The quality of these surface-data-based 3-D models is then tested with respect to the fit of the predictions with the underground appearance of faults. All three extrapolation approaches result in a close fit ( > 10 %) when compared with underground rock laboratory mapping. Subsequently, we performed a statistical interpolation based on Bayesian inference in order to validate and further constrain the uncertainty of the extrapolation approaches. This comparison indicates that fieldwork at the surface is key for accurately constraining the geometry of the fault pattern and enabling a proper extrapolation of major faults towards depth. Considerable uncertainties, however, persist with respect to smaller-sized secondary structures because of their limited spatial extensions and unknown reoccurrence intervals.
Highlights
Geological information is inherently three-dimensional (3D) in space but often represented in 2-D (Jones et al, 2009)
The central Aar massif (Central Switzerland) serves as a study area where we investigate the 3-D geometry of the Alpine fault pattern by means of both surface and underground ground information
In order to represent the 3-D geometry of faults, we developed a workflow based on a combination of remote sensing and fieldwork (Fig. 3)
Summary
Geological information is inherently three-dimensional (3D) in space but often represented in 2-D (Jones et al, 2009). Bistacchi et al, 2008; Caumon et al, 2009; Hassen et al, 2016; Sausse et al, 2010; Stephens et al, 2015). Explicit structural modelling can further be subdivided into stochastic and deterministic methods. Stephens et al, 2015), whereas as in stochastic approaches parameters are defined by a probabilistic density function with a component of randomness R. Schneeberger et al.: 3-D structural modelling in crystalline rocks tunnels or boreholes. Known information is extrapolated towards the unknown. At the time of extrapolation, the validity cannot be proven unless additional information, such as geophysical, borehole, or excavation data, is integrated
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