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-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
Dataset status	Complete
Activity type	numerical simulation
Access constraint	Open
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Quality statement	Quality controlled by manual inspection
Quality Editor	Editor name: Zbigniew Czechowski Editor Institution: of Geophysics, Polish Academy of Sciences
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Dataset DOI	https://doi.org/
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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