For an extensive review of the data collection and cleaning process, as well as graphical exploration of the data, codes and figures are presented in the complementary project: https://github.com/gabipana7/seismic-exploratory-data-analysis
First of all networks are generated and their connectivity distribution is analyzed.
This is done with networks_parameter_dependency.jl
With networks_generator.jl networks are generated for the best cube sizes, obtained from the parameter dependency.
With motifs_generator.jl triangle and tetrahedron motifs are generated for these networks, with a cutoff for micro-earthquakes (data is trimmed to satisfy that magnitude > 2).
This computation is done with a slightly adapted Python version of the Nemomap software (https://github.com/zicanl/NemoMapPy)
With motifs_analysis.jl the motifs are analyzed as such:
Triangles
- the area of each individual motif is calculated
- for each individual motif, the total (and mean) energy released by the earthquakes that occured in each of its nodes is computed
- for each individual motif, the area is weighted by its total(mean) energy
- the distribution of the results is computed and analyzed with the Python Powerlaw package
Tetrahedrons
- the volume of each individual motif is calculated
- for each individual motif, the total (and mean) energy released by the earthquakes that occured in each of its nodes is computed
- for each individual motif, the volume is weighted by its total(mean) energy
- the distribution of the results is computed and analyzed with the Python Powerlaw package
This repository represents the toolbox support of our scientific article available at: https://www.sciencedirect.com/science/article/abs/pii/S0378437123008567
Cite as:
Gabriel Tiberiu Pană, Alexandru Nicolin-Żaczek, Motifs in earthquake networks: Romania, Italy, United States of America, and Japan, Physica A: Statistical Mechanics and its Applications, Volume 632, Part 1, 2023, 129301, ISSN 0378-4371, https://doi.org/10.1016/j.physa.2023.129301