DERIVATION AND VERIFICATION OF A STRUCTURAL 3D MODEL OF THE HASLITAL (AAR MASSIF, SWITZERLAND) FROM REMOTE SENSING AND FIELD DATA

Ideally, the three-dimensional (3D) representation of subsurface structures relies on both underground and surface data. Commonly, the former is scarcely available or entirely lacking. Therefore, 3D models often have to rely on surface data only, requiring the projection of geological surface information to depth. The prediction of subsurface continuation of rocks and structures represents an important task for general research and applied projects (e.g., underground constructions, deep geothermal energy, fossil energy and ore resources, CO2 sequestration, and nuclear waste disposal). Despite the progress in the development of powerful software tools and construction capabilities, development of 3D geological models still mainly depends on amount and quality of surface information available and related assumptions regarding the projection to depth. In this respect, estimations about quality and associated uncertainty of information projected to depth and transfer of this knowledge to the end user of the 3D model are crucial.

In this study we investigate the large-scale 3D deformation pattern in the European Central Alps in Switzerland from surface data only combined with a thorough assessment of uncertainties related to input data, to extrapolation of surface data and the correlation of surface and underground information. We bank our approach on the mapping of lineaments by means of remote sensing and field work. Our uncertainty estimations concentrate on aspects related to both 2D and 3D input data. We introduce the concepts of the Central Extrapolation Surface (CES) and the Extrapolation Uncertainty Area/Volume (2D: EUA; 3D: EUV), which enable the valid projection of surface data to depth within a well-defined and data-constraint uncertainty range. The projection accuracy is evaluated using geological information from depth available from a gas pipeline tunnel. With this study we document, how geometrical correlations between surface and underground data may be used to construct a valid 3D model and we demonstrate that this approach delivers geologically relevant results. The application of the suggested work flow will help for structural predictions at depth being helpful in the case of underground constructions but also for improved geodynamic understanding of mountain building processes.