Data Publication

Digital Volume Correlation (DVC) data from an analogue experiment exploring kinematic coupling of brittle and viscous deformation

Schmid, Timothy | Adam, Jürgen | Zwaan, Frank | Schreurs, Guido | Rosenau, Matthias | Hollis, Dave

GFZ Data Services

(2024)

This dataset includes volumetric data sets from a Digital Volume Correlation (DVC) analysis for recreating images of a re-analyzed analogue models previously presented in (Zwaan et al., 2018). Using a brittle-viscous two-layer setup, this experiment focused on the evolution of a rift-pass structure. On top of the viscous layer, two viscous seeds are placed with a right-stepping stair-case offset to simulate two propagating rift segments, confining a central rift-pass block (Fig. 1). The selected model was analyzed by means of Digital Volume Correlation (DVC) applied on X-Ray computed tomography (XRCT) volumes. The data set includes DVC data in the form of .mat files for incremental (i.e., 20 min intervals) and cumulative displacement components. In addition, this dataset provides a MATLAB script for 1) recreating volumetric displacement sets of subsequent time steps 2) calculating finite stretches and 3) rigid-body rotations. The used experiment was performed at the Tectonic Modelling Laboratory of the University of Bern (UB). DVC analysis was performed at the Royal Holloway University London (RHUL). The model consists of a two-layer brittle-viscous set up with a total thickness of 8 cm and the set up lies on top of a 5 cm thick foam-plexiglass base with a length and width of 800 mm by 305 mm, respectively. Before model construction, the foam-plexiglass assemblage is placed between longitudinal side walls and expands during the course of the experiment as the mobile sidewalls move apart. The applied divergence velocity is 7.5 mm/h and with has an orthogonal direction with respect to the viscous seeds. This results in a maximum displacement of 30 mm after a total run time of 4h. Detailed descriptions of the experiment, mechanical properties as well as monitoring techniques can be found in Schmid et al. (2024).

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