Data Publications

Discrete Element Method model data of biaxial shear deformation

  • Deutsches GeoForschungsZentrum GFZ
  • 2018
  • Discrete Element Method model data of biaxial shear deformation
  • 10.5880/fidgeo.2018.008
  • EPOS
  • multi-scale laboratories
  • rock and melt physical properties
  • European Plate Observing System
  • Biaxial shear deformation
  • Discrete Element Method
  • Stick-slip mechanics
  • Owing to their destructive potential, earthquakes receive considerable attention from laboratory studies. In friction experiments, stick-slips are studied as the laboratory equivalent of natural earthquakes, and numerous attempts have been made to simulate stick-slips numerically using the Discrete Element Method (DEM). However, while laboratory stick-slips commonly exhibit regular stress drops and recurrence times, stick-slips generated in DEM simulations are highly irregular. This discrepancy highlights a gap in our understanding of stick-slip mechanics, which propagates into our understanding of earthquakes. In this work, we show that regular stick-slips emerge in DEM when time-dependent compaction by pressure solution is considered. We further show that the stress drop and recurrence time of stick-slips is directly controlled by the kinetics of pressure solution. Since compaction is known to operate in faults, this mechanism for frictional instabilities directly relates to natural seismicity. The zip-fle contains a Python script ( that is used to generate the data fgures as reported by Van den Ende & Niemeijer (2018), auxiliary script fles in the scripts directory, and the original model data in ASCII and HDF format in the data directory. The main Python script fle will read and process the original model data and generate the interactive data fgures. These fgures are automatically saved in PDF format. More information is given in Van den Ende & Niemeijer (2018) to which these data and scripts are supplementary material to.
GCMD Sciencekeywords describing the dataset. Click on Keyword to find similar datasets