3D fold and fault reconstruction with an uncertainty model: An example from an Alpine tunnel case study

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Abstract

In order to improve the railway connection between Austria and Italy, a base tunnel, extending from Fortezza to Innsbruck (57 km), is under study. The design corridor crosscuts a large and strongly tectonized section of the Eastern Alpine chain, characterized by complex metamorphic and igneous lithology and polyphase structures developed under ductile to brittle deformation conditions. In order to model the sub-surface geology of the area, surface and sub-surface geological data have been integrated in a spatial database. 3D geological models of the Italian part of the corridor have been constructed on the basis of this data using two approaches. The first is a more traditional approach, involving the reconstruction of several parallel and intersecting cross-sections. It has been implemented using ArcGIS® software with custom-developed scripts that enable one to automatically project structural data, collected at the surface and along boreholes, onto cross-sections. The projection direction can be controlled and is based on structural trends obtained from a detailed statistical analysis of orientation data. Other ArcGIS® scripts enable linking of the network of crosscutting profiles and help to secure their consistency. The second approach involves the compilation of a true 3D geological model in gOcad®. As far as time efficiency and visualization are concerned, the second approach is more powerful. The basic structural geology assumptions, however, are similar to those applied in the first approach. In addition to the 3D model, compilation scripts (ArcGIS® and gOcad®) have been developed, which allow estimation of the uncertainties in the depth extrapolation of structures observed at the surface or along boreholes. These scripts permit the assignment of each projected structural element (i.e., geological boundaries, faults and shear zones) to a parameter estimating reliability. Basic differences between “data-driven” interpolation and “knowledge-based” extrapolation of geological features at depth are also discussed and consequences for the uncertainty estimates of 3D geological models are evaluated.

Section snippets

Introduction and motivation

3D geological models that are based on field data aim at predicting geological conditions at depth, but are strongly affected by different sources of uncertainty. These sources include the overall structural and stratigraphic complexity of the site, the required depth of the prediction, topography, regional attitude of geological structures and the continuity of bedrock outcrops.

Traditionally, sub-surface models are based on a network of cross-sections, sometimes constructed using sound

Regional framework

The Alps originated by subduction and closure of the Mesozoic Tethyan ocean (Cretaceous–Eocene) and subsequent collision between the European passive continental margin and the Adriatic (African) active plate margin (Dal Piaz et al., 2003, and refs. therein). This geodynamic evolution generated a collisional wedge that in the Eastern Alps (Fig. 1) consists, from top to bottom, of (1) the Adria-derived Austroalpine continental basement and cover nappe system; (2) the Penninic system exposed in

3D geological modeling

Geological modeling for the sector between Fortezza (N of Bressanone) and the Italy–Austria political boundary was carried out taking into account structural data and detailed geological maps organized in a geological database (GeoDB) implemented in ArcGIS®. Structural stations were located and georeferenced in 2D with the help of a GPS, whilst elevations were determined by means of projection onto a high-resolution digital elevation model (DEM). The polygon and polyline features of the

Evaluation of geological uncertainty

When dealing with the estimation of uncertainties associated with the projection to depth of geological structures observed at surface, there is an obvious difference between interpolation of surfaces within the domain where data points are present, and extrapolation outside of this domain. In interpolation, the shape of the surface is strongly constrained by the data themselves and uncertainty can be defined on a rigorous statistical basis (geostatistics). In contrast, when it is necessary to

Discussion

The 3D geological modeling of a wide sector of the eastern Alps carried out using two different methodologies (pseudo-3D modeling in ArcGIS® environment and a true 3D model with gOcad®) has highlighted the fundamental difference between “data-driven” interpolation and “knowledge-based” extrapolation, a difference that needs to be considered in uncertainty estimates. In the case of interpolation, the shape of the interpolated surface is strongly constrained by the data themselves and uncertainty

Acknowledgments and notes on the project

Field data used in this project were collected during two mapping campaigns, in 2000 and 2004, funded by an EU research grant and by BBT SE, the Italian–Austrian railway company in charge of the Brenner Basistunnel project, whose kind help is acknowledged. A first draft of the geological model described here was developed by the CFR (Consorzio Ferrara Ricerche) working group and delivered to BBT SE as a result of this project. This model was developed in 2D, using a network of interconnected

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