2026-04-21T02:15:23Zhttp://doidb.wdc-terra.org/oaip/oai

oai:doidb.wdc-terra.org:72172021-11-11T12:43:54ZDOIDBDOIDB.GFZ

false4DOIDB.GFZ 10.5880/GFZ.fidgeo.2021.029 Cooke, Michele Michele Cooke 0000-0002-4407-9676 University of Massachusetts, Amherst, US Elston, Hanna Hanna Elston 0000-0002-2420-5241 University of Massachusetts, Amherst, US Chaipornkaew, Laainam Laainam Chaipornkaew 0000-0003-2021-3655 Stanford University, Stanford, US GFZ Data Services 2021 EPOS multi-scale laboratories analog models of geologic processes analog modelling results software tools analog experiments fault evolution strike-slip wet kaolin Clay deformation > shearing Digital Image Correlation (DIC) / Particle Image Velocimetry (PIV) Matlab (Mathworks) Shear box SLR camera Surface image tectonic setting > plate margin setting > transform plate boundary setting wrench fault Cooke, Michele Michele Cooke 0000-0002-4407-9676 University of Massachusetts, Amherst, US Elston, Hanna Hanna Elston 0000-0002-2420-5241 University of Massachusetts, Amherst, US Chaipornkaew, Laainam Laainam Chaipornkaew 0000-0003-2021-3655 Stanford University, Stanford, US Geomechanics Physical Modeling Lab (University of Massachusetts Amherst, US) University of Massachusetts, Amherst, US Cooke, Michele University of Massachusetts, Amherst, US 2021-07-01 Dataset 10.1002/essoar.10507909.1 10.1080/2151237X.2007.10129236 https://www.mathworks.com/matlabcentral/fileexchange/27659-pivlab-particle-image-velocimetry-piv-tool 1 CC BY 4.0 The data set includes the digital image correlation of 16 dextral strike-slip experiments performed at the University of Massachusetts at Amherst (USA). The DIC data sets were used for a machine learning project to build a CNN that can predict off-fault deformation from active fault trace maps. The experimental set up and methods are described with the main text and supplement to Chaipornkaew et al (in prep). To map active fault geometry and calculate the off-fault deformation we use the Digital Image Correlation (DIC) technique of Particle Image Velocimetry (PIV) to produce incremental horizontal displacement maps. Strain maps of the entire region of interest can be calculated from the displacements maps to determine the fault maps and estimate off-fault strain throughout the Region of Interest (ROI). We subdivide each ROI into five subdomains, windows, for training the CNN. This allows a larger dataset from the experimental results. The data posted here include the incremental displacement time series and animations of strain for the entire ROI.
We document the evolution of dextral strike-slip faults within wet kaolin loaded within a split box. All experiments used a 2.5 cm claybox but we varied 1) the loading rate from 0.25 to 1.5 mm/min, and the localized or distributed basal shear (abutting basalt plates or 2.5 cm wide elastic sheet respectively). All experiments were repeated twice. Red and black sand grains distributed on the surface of the clay provide the pixel variation that allow us to calculate the incremental horizontal displacement fields from the photos of the clay surface using DIC techniques. The distribution of sand and timing of photos are set to optimize both data resolution and displacement uncertainty. allows for <0.01 mm uncertainty of horizontal displacement between successive images of our experiment measured at points with spacing of 1 mm.

We use the matlab based PIVlab (Thielicke,2019) with a fast Fourier transform three-pass filter to optimize displacement resolution. Through the three passes with linear interpolation, the initial window size of 64 pixels reduces to 16 pixels, which corresponds to incremental displacement data every 0.89 mm. The resulting displacements vary along the edges of the ROI far from faults with mean standard of deviation of <0.01 mm. We consider this to be the uncertainty of the incremental displacements.