Speaker
Description
The geological characterization of the Italian candidate site for the Einstein Telescope (ET) in Sardinia relies on integrating heterogeneous datasets to support the engineering design and geomechanical characterization of the observatory. A 3D geological model, derived from industrial data and primary structural constraints, is already available and serves as the deterministic baseline for the region. This initial model, primarily developed through explicit modeling techniques, provides a fundamental geometric reference. We present the evolution of this modeling strategy, which aims to refine the industrial baseline with an implicit stochastic model strategy by integrating dedicated research surveys, including high-resolution surface geological mapping, deep boreholes, and airborne electromagnetic (AEM) acquisition. The current workflow marks a methodological transition from explicit reconstructions toward an implicit probabilistic framework using GemPy integrated into a custom QGIS-based environment designed for geological data management and probabilistic simulation. By leveraging GemPy’s stochastic engines, constrained by the geometric primitives of the industrial reference model, we will generate multiple realizations of the geological volumes, enabling a rigorous quantification of structural and lithostratigraphic uncertainties. A key focus of this refinement is populating the 3D model with technical attributes and integrating Discrete Fracture Networks (DFN). These elements are critical for characterizing rock mass quality and hydraulic behavior in the sectors identified for tunnel and cavern construction. This integrated approach ensures that the final 3D model serves as a dynamic, reliable tool for the ET infrastructure.