Geospatial Interpolation and Geological Complexity in Data-Driven Ground Models

Summary The in-depth integration of sparse 1D geotechnical data with 2D UHR seismic reflection in a consistent geological framework forms the back-bone of the data-driven ground model approach. In order to predict CPT or geotechnical parameters (and their uncertainties) across the entire development area, one typically relies on geostatistical methods, like 3D kriging, Considering that the 2D line spacing is often larger than key geological phenomena, this interpolation will lead to uncertainty. In this paper, we investigate the effect of line spacing and geological complexity on the model prediction, using the TNW site (offshore the Netherlands) as a case study. We focus on an area with ultra-high-resolution 3D data, and decimate the volumes of 4 sub-sets with distinct geological features and complexity in order to assess the uncertainty on the interpolation, using both geostatistical and machine learning methods.