Description: 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.
Global identifier:
Doi(
"10.5880/GFZ.fidgeo.2021.029",
)
Origins: /Wissenschaft/Helmholtz-Gemeinschaft/GFZ
Tags: USA ? Karte ? Lehm ? Daten ? Zeitreihe ? Partikel ? Digital Image Correlation (DIC) / Particle Image Velocimetry (PIV) ? EPOS ? Matlab (Mathworks) ? SLR camera ? Shear box ? Surface image ? analog experiments ? analog modelling results ? analog models of geologic processes ? deformation > shearing ? fault evolution ? multi-scale laboratories ? software tools ? strike-slip ? tectonic setting > plate margin setting > transform plate boundary setting ? wet kaolin ? wrench fault ?
License: cc-by/4.0
Language: Englisch/English
Issued: 2021-01-01
Time ranges: 2021-01-01 - 2021-01-01
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