The data set includes the 3D incremental displacement fields resulting from Digital Image Correlation (DIC) analysis of four strike-slip experiments performed at the laboratory for experimental tectonics at GFZ Helmholtz Centre for Geosciences in Potsdam in 2022. The data here include the incremental displacement time series from four strike-slip experiments with quartz sand G12 (Rosenau et al., 2018): two with 1.2 cm thick sandpack and two with a 3 cm thick sandpack. Ramos Sánchez et al. (in review) used the incremental horizontal displacement fields from strike-slip fault experiments within different materials to train Convolutional Neural Networks (CNN) to predict off-fault deformation from active fault trace maps. Because off-fault deformation is difficult to ascertain in the field, convolutional neural networks trained on data from scaled physical experiment that simulate upper crustal deformation can inform how much off-fault deformation can be expected along crustal strike-slip faults.
For training the CNNs Ramos Sánchez et al. (in review) used incremental horizontal displacement fields from experiments in wet kaolin (Cooke et al., 2021) and both poured and sedimented CV32 sand (Visage et al., 2023). All experiments used identical conditions of straight basal velocity discontinuity to produce overlying strike-slip faults. The benefit of training the CNN on strike-slip experiments within different materials is to capture a wide range of strike-slip deformation that may occur within the upper crust. After training of the CNN Ramos Sánchez et al. (in review) tested the trained on unseen fault maps including maps from the two 3 cm thick G12 sand experiments of this dataset. The 1.2 cm sandpack maps were excluded from the study because the strike-slip faults were very fine and closely spaced so they were not as well resolved as those of 3 cm sandpack. By testing the CNN trained on CV32 fault maps with the fault maps from G12 sand experiments, Ramos Sanchez et al. were able to assess if the applicability of the CNN to experiments with similar but not identical sand.
Information on the displacement field analysis and CNN training and testing can be found in the main text and supplement to Ramos Sánchez et al. (in review). The file structure of this zip folder is fully described in the list of files.