This dataset presents the raw data of an experimental series of centrifuge models performed to test the influence of pre-existing weak zones in the lower crust (herein after referred to as Weak Lower Crust –WLC) during continental compression. We varied the width of the WLC, the dip of the interfaces bounding the WLC and the frictional properties at the WLC-LC interface by using lubricant (vaseline).
In this dataset, we provide four different types of data, that can serve as supporting material and can be used for further analysis: 1) The top-view photos, taken at different stages and showing the deformation process of each model; 2) Digital Elevation Models (DEMs) used to reconstruct the 3D deformation of the performed analogue models; 3) Line-drawing of fault and fracture patterns to be used for fault statistical quantification; 4) A Python script to draw swath profiles (outputs) of the analogue models.
Further details on the modelling strategy can be found in the publication associated with this dataset and in Milazzo et al. (2021), using a similar setup for achieving compression in the centrifuge. Materials used for these analogue models were described in Corti (2012), Montanari et al. (2017), Del Ventisette et al. (2019), Zou et al. (2024) and Wan et al. (2025).
This data set includes the results of digital image correlation (DIC) of two experiments on gravitational tectonics at passive margins performed at the Helmholtz Laboratory for Tectonic Modelling (HelTec) of the GFZ German Research Centre for Geosciences in Potsdam in the framework of EPOS transnational access activities in 2018. The experiments aim at documenting the difference in structural evolution between two tilting scenarios: Instant versus progressive, or fast versus slow. Detailed descriptions of the experiments and results can be found in Ge et al. (2019, Geology) to which this data set is supplement. The DIC analysis yields quantitative deformation information of the experiment surfaces by means of 3D surface displacements from which strain has been calculated. The data presented here are visualized as surface uplift and strain maps, strain evolution maps and surface velocity time-series. Python scripts for visualization of data are appended. Parts of this data (see list of files) are derived from Ge et al. (2019, http://doi.org/10.5880/GFZ.4.1.2019.001).
This data set includes 40 videos (+ 1 image) depicting the surface evolution of 39 experiments on crustal extension, as well as 4D CT imagery (figures and videos) of 6 of these experiments. The experiments examined the influence of the method for driving extension (foam base, rubber base, plate base or conveyor base) for localization of deformation in overlying layers of brittle-only and brittle-viscous materials representing the earth’s crust. All experiments were performed at the Tectonic Modelling Laboratory of the University of Bern. Detailed descriptions of the experiments and monitoring techniques can be found in Zwaan et al. (2019) to which these data are supplementary material.
All experiments were monitored with top view photographs (SLR camera Nikon D-100 6.1 MPx). The photograph time steps depend on the applied extension velocity, but are generally 1 or 2 min. Six experiments were also monitored with an X-Ray computed tomography technique using a 64 slice Siemens Somatom Definition AS X-ray CT-scanner (Zwaan et al., 2016) with varying time intervals (5-30 min). CT-data was analyzed with the software OsiriX (Pixmeo SARL).
This data set includes the results of digital image correlation of three experiments on gravitational tectonics at passive margins performed at the Helmholtz Laboratory for Tectonic Modelling (HelTec) of the GFZ German Research Centre for Geosciences in Potsdam in the framework of EPOS transnational access activities in 2018. Detailed descriptions of the experiments and monitoring techniques can be found in Ge et al. (submitted) to which this data set is supplement. The DIC analysis yields quantitative deformation information of the experiment surfaces by means of 3D surface displacements from which strain has been calculated. The data presented here are visualized as surface displacement maps, strain maps and strain evolution maps.