Understanding the geological and geotechnical characteristics of the shallow subsurface is crucial for engineering decisions in densely populated regions like the Netherlands. Urban areas are for a large part built on a complex stack of sand, clay, silt, gravel and organics that were deposited over the last hundreds of thousands of years under gradually subsiding tectonic conditions. One of these units, a fluvially deposited sand known as the Kreftenheye Formation, is widespread across the western part of the country and is vital for foundation design and groundwater dynamics. The Kreftenheye Formation is therefore well described by geotechnical and geological tests, yet integrating both types of data has rarely been done on a nationwide scale.

This paper focuses on 76 pairs of Cone Penetration Tests and boreholes that include Kreftenheye, out of a full database of nearly 200 pairs from TNO - Geological Survey of the Netherlands, generally reaching depths up to 40 meters. The study reveals significant spatial variations and depositional patterns within the Kreftenheye Formation, illustrating how overlying or interlayered flood plain or soft channel lag sediments influences the response of the CPT. Furthermore, CPT response and borehole samples have helped with making geological distinctions between the upper, Weichselian part of the Kreftenheye Formation and the lower Saalian part, showing how integrating both datasets can give both geological and geotechnical insights.@en

Reclaimed coastal plains often experience significant subsidence as a result of phreatic water level lowering, which induces oxidation of organic material, shrinkage of clay, and sediment compaction. A primary example in the center of the Netherlands, is the 'South Flevopolder' which was reclaimed in 1968 and transformed into an area for residential, industrial and agricultural use. The area subsided over 1.5 m since its reclamation and its surface is still lowering.The city of Almere, with roughly 200.000 inhabitants and a surface area of c. 250 km2, is situated in the South Flevopolder. Most buildings in the city are founded in deeper Pleistocene sand, whilst objects such as parking lots, sport fields and playgrounds are often unfounded and are directly situated on the younger Holocene coastal deposits. Currently, the unfounded objects show subsidence rates as high as 5 mm per year for which the different subsidence rates may be related to subsurface heterogeneities. The upper layers in the area are dominated by clay and sand, up to a few meters in thickness, which overly peat and highly organic layers. The lowering of the phreatic surface results in an erratic pattern of subsidence over the area.We present a workflow to disentangle and parameterize the different contributions of shallow subsidence from Interferometric synthetic-aperture radar (InSAR) measurements. InSAR measurements from founded and unfounded scatterers are separated with a dimensionality reduction technique, t-Distributed Stochastic Neighbor Embedding (t-SNE), followed by an automatic detection of clusters with Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). We have limited ourselves to structures with a construction date of >10 years with respect to the first InSAR measurement date to reduce the effect of construction-related consolidation and isolate the effect of shallow subsidence related to reclamation and phreatic surface level changes. The filtered dataset represents the surface response of unfounded structures in the urbanized reclaimed coastal area.The subsidence processes are disentangled and parameterized with an Ensemble Smoother with Multiple Data Assimilation (ES-MDA). Additional input data for this method is provided by a phreatic groundwater level model and a voxelized lithological model from the surface towards the top of the Pleistocene sand layers.We show that the automated data selection method prevents bias by selecting unfounded objects and the proposed workflow can be of aid when studying shallow subsidence in urbanized areas, where most objects are founded below the level at which shallow subsidence takes place. The results of this study quantify the rate of the different subsidence processes on a spatiotemporal scale and thus provide insights for tailored decision making to mitigate subsidence.@en

The coastal plains of the Netherlands are subject to anthropogenic and natural subsidence with rates which are an order of magnitude higher than sea-level rise. Because one-third of the Netherlands lies below mean sea level, subsidence may threaten the country’s subsistence with major socio-economic consequences. Subsidence is a normal natural process but is overprinted and accelerated by anthropogenic activities which drives deep or shallow processes. Deep processes are caused by the extraction of hydrocarbons and salt, whereas shallow processes are primarily caused by lowering of phreatic groundwater levels. At present, the relative contribution of each process to total subsidence (i.e. natural plus anthropogenic) is unclear. Such information is important for stakeholders to support decision making on subsidence mitigation and it should be substantiated with independent scientific studies.

We present the outline of a hybrid Artificial Intelligence (AI) big data and model workflow to disentangle different subsidence forcing and we report on preliminary results for an area covering a gas field in the peat-rich Friesland coastal plain. The proposed workflow is a hybrid approach between a knowledge-based physical model and machine-learning techniques. The big input data comprises a suite of static (structural model) and time-dependent subsurface data (phreatic groundwater level, reservoir pressure), and geodetic measurements. Geomechanical models provide the connection between the drivers (groundwater levels and reservoir pressures) and the surface movement.@en

The Netherlands is subject to anthropogenic and natural subsidence with rates for which the relative contribution of each process to total subsidence (i.e. natural plus anthropogenic) is still unclear. Such information is important for stakeholders to support decision making on subsidence mitigation and it should be substantiated with independent scientific studies. We present the outline of a hybrid Artificial Intelligence (AI) and knowledge-based physical model (geomechanical models) workflow to disentangle different subsidence forcing. We use static (structural model) and time-dependent data for both surface (geodetic measurements: GPS, levelling and InSAR) and subsurface (phreatic groundwater level, reservoir pressure). We show preliminary results of the approach for an area covering a gas field in the Friesland coastal plain and an area in the peat-rich central Rhine-Meuse delta plain. @en

The Netherlands is subject to anthropogenic and natural subsidence with rates for which the relative contribution of each process to total subsidence (i.e. natural plus anthropogenic) is still unclear. Such information is important for stakeholders to support decision making on subsidence mitigation and it should be substantiated with independent scientific studies. We present the outline of a hybrid Artificial Intelligence (AI) and knowledge-based physical model (geomechanical models) workflow to disentangle different subsidence forcing. We use static (structural model) and time-dependent data for both surface (geodetic measurements: GPS, levelling and InSAR) and subsurface (phreatic groundwater level, reservoir pressure). We show preliminary results of the approach for an area covering a gas field in the Friesland coastal plain and an area in the peat-rich central Rhine-Meuse delta plain. @en