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International audience; The complex conductivity of soils remains poorly known despite the growing importance of this method in hydrogeophysics. In order to fill this gap of knowledge, we investigate the complex conductivity of 71 soils samples (including four peat samples) and one clean sand in the frequency range 0.1 Hz to 45 kHz. The soil samples are saturated with six different NaCl brines with conductivities (0.031, 0.53, 1.15, 5.7, 14.7, and 22 S m-1, NaCl, 25°C) in order to determine their intrinsic formation factor and surface conductivity. This data set is used to test the predictions of the dynamic Stern polarization model of porous media in terms of relationship between the quadrature conductivity and the surface conductivity. We also investigate the relationship between the normalized chargeability (the difference of in-phase conductivity between two frequencies) and the quadrature conductivity at the geometric mean frequency. This data set confirms the relationships between the surface conductivity, the quadrature conductivity, and the normalized chargeability. The normalized chargeability depends linearly on the cation exchange capacity and specific surface area while the chargeability shows no dependence on these parameters. These new data and the dynamic Stern layer polarization model are observed to be mutually consistent. Traditionally, in hydrogeophysics, surface conductivity is neglected in the analysis of resistivity data. The relationships we have developed can be used in field conditions to avoid neglecting surface conductivity in the interpretation of DC resistivity tomograms. We also investigate the effects of temperature and saturation and, here again, the dynamic Stern layer predictions and the experimental observations are mutually consistent.

The shallow subsurface of Groningen, the Netherlands, is heterogeneous due to its formation in a Holocene tidal coastal setting on a periglacially and glacially inherited landscape with strong lateral variation in subsurface architecture. Soft sediments with low, small-strain shear wave velocities (VS30 around 200ms−1) are known to amplify earthquake motions. Knowledge of the architecture and properties of the subsurface and the combined effect on the propagation of earthquake waves is imperative for the prediction of geohazards of ground shaking and liquefaction at the surface. In order to provide information for the seismic hazard and risk analysis, two geological models were constructed. The first is the ‘Geological model for Site response in Groningen’ (GSG model) and is based on the detailed 3D GeoTOP voxel model containing lithostratigraphy and lithoclass attributes. The GeoTOP model was combined with information from boreholes, cone penetration tests, regional digital geological and geohydrological models to cover the full range from the surface down to the base of the North Sea Supergroup (base Paleogene) at ∼800m depth. The GSG model consists of a microzonation based on geology and a stack of soil stratigraphy for each of the 140,000 grid cells (100m × 100 m) to which properties (VS and parameters relevant for nonlinear soil behaviour) were assigned. The GSG model serves as input to the site response calculations that feed into the Ground Motion Model. The second model is the ‘Geological model for Liquefaction sensitivity in Groningen’ (GLG). Generally, loosely packed sands might be susceptible to liquefaction upon earthquake shaking. In order to delineate zones of loosely packed sand in the first 40m below the surface, GeoTOP was combined with relative densities inferred from a large cone penetration test database. The marine Naaldwijk and Eem Formations have the highest proportion of loosely packed sand (31% and 38%, respectively) and thus are considered to be the most vulnerable to liquefaction; other units contain 5–17% loosely packed sand. The GLG model serves as one of the inputs for further research on the liquefaction potential in Groningen, such as the development of region-specific magnitude scaling factors (MSF) and depth–stress reduction relationships (rd).

Over the last ten to twenty years, geological surveys all over the world have been entangled in a process of digitisation. Their paper archives, built over many decades, have largely been replaced by electronic databases. The systematic production of geological map sheets is being replaced by 3D subsurface modelling, the results of which are distributed electronically. In the Netherlands, this transition is both being accelerated and concluded by a new law that will govern management and utilisation of subsurface information. Under this law, the Geological Survey of the Netherlands has been commissioned to build a key register for the subsurface: a single national database for subsurface data and information, which Dutch government bodies are obliged to use when making policies or decisions that pertain to, or can be affected by the subsurface. This requires the Survey to rethink and redesign a substantial part of its operation: from data acquisition and interpretation to delivery. It has also helped shape our view on geological surveying in the future.The key register, which is expected to start becoming operational in 2015, will contain vast quantities of subsurface data, as well as their interpretation into 3D models. The obligatory consultation of the register will raise user expectations of the reliability of all information it contains, and requires a strong focus on confidence issues. Building the necessary systems and meeting quality requirements is our biggest challenge in the upcoming years. The next step change will be towards building 4D models, which represent not only geological conditions in space, but also processes in time such as subsidence, anthropogenic effects, and those associated with global change.

The Geological Survey of the Netherlands aims at building a 3D geological property model of the upper 30 meters of the Dutch subsurface. This model, called GeoTOP, provides a basis for answering subsurface related questions on, amongst others, sand and gravel resources. Modelling is carried out per province using a core-database containing several hundreds of thousands of core-descriptions, the majority of which reach down to around 10 to 15m, and a context of geological maps. This study focuses on the model of the province of Zuid-Holland where major cities like Rotterdam and The Hague are situated and the Rivers Rhine and Meuse enter the North Sea. A stepwise procedure consisting of automated database queries, 2D modelling of stratigraphic surfaces and 3D property modelling, resulted in a model of 50 million volume cells, each measuring 100 by 100 meters in horizontal directions and 0.5 meters in the vertical direction, every cell having estimates of lithology (sand, clay, peat) and sand-grain size class data. Running simulations, using the Sequential Indicator Simulation algorithm, resulted in information on the probabilities of the aforementioned properties. In the study presented here we focussed on two subjects. First we compared the above described GeoTOP model with two "quick and dirty" models constructed several years ago. The most important and visible difference between those models is the geological framework model used, which has more detail in GeoTOP. By comparing the two, we show the effect of detailed modelling on the cumulative exploitable reserves of aggregates. Secondly we show the effect of data density on GeoTOP modelling results. Using GeoTOP of Zuid-Holland gives us a opportunity to study the effect on the results of the amount of data used. We have therefore carried out a validation using subsequently less borehole information to estimate properties on a constant validation set. Organisations: Golder Magyarorszag ZRt.; Mecsekerc Zrt.; MOL