Predictions of Numerical Earthquakes

Datasets for applying supervised machine learning to Discrete Element Method (DEM) simulations of laboratory earthquakes on a numerical fault. Part 1. The dataset from 1680 DEMs simulations - independent variables and dependent variable for supervised machine learning algorithms. The dataset was used to predict the Macroscopic Friction Coefficient (MFC) in a numerical fault. Four variables described the initial state of the system: compressive force (CF), shearing velocity (SV), material density (MD), and granular layer thickness (GLT). Part 2. The dataset to predict friction coefficient of numerical fault at any time of the DEM simulation. Part 3. The dataset to predict time to next slip event at any time of the DEM simulation of a numerical fault.

Data and Resources

Additional Info

Field Value
GCMD keywords
Dataset Center https://dataportal.igf.edu.pl
Maintainer Data Steward
Dataset PI
  • PI name: Piotr Klejment PI email: pklejmentfoo(at)igf.edu.pl PI Institution: Institute of Geophysics, Polish Academy of Sciences PI Department: Theoretical Geophysics
Dataset Owner
  • Owner name: Institute of Geophysics, Polish Academy of Sciences Owner PIC (Participant Identification Code): 996625337 Owner address: ul. Ksiecia Janusza 64, 01-452 Warsaw, Poland
Licence Creative Commons Attribution 4.0 International (CC BY 4.0)
Dataset status Complete
Activity type numerical simulation
Access constraint Open
Embargo
Dataset modified
Metadata created
Quality statement Quality controlled by manual inspection
Quality Editor
  • Editor name: Zbigniew Czechowski Editor Institution: of Geophysics, Polish Academy of Sciences
Spatial distribution
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Start time
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Dataset citation
Dataset DOI 10.25171/InstGeoph_PAS_IGData_Predictions_of_Numerical_Earthquakes
External resource
Publication
Project
  • Project title: Prediction of numerically modeled earthquakes using supervised machine learning Project ID: MINIATURA 6 Nr DEC-2022/06/X/ST10/01581 Project financing institution: National Science Centre