Codes and Data Processing Workflow for "A Multiscaling Fingerprint of Earthquake Diffusion in Seismic Swarms"

Le résumé fourni par la source

# Code and Data for A Multiscaling Fingerprint of Earthquake Diffusion in Seismic Swarms Authors:Cataldo Godano, Giuseppe Petrillo This repository contains the source codes used to reproduce the analyses presented in the manuscript. --- ## Repository contents ### `swarm_decl.f90` Fortran program used to identify persistent earthquake swarms from the relocated Southern California earthquake catalog. The workflow consists of: 1. Space-time clustering using a DBSCAN-like algorithm.2. Repeated clustering over multiple parameter combinations.3. Identification of swarm-like clusters using the temporal moment-release criteria proposed by Passarelli et al. (2026):4. Consensus analysis over all clustering realizations.5. Extraction of persistent swarms from the consensus graph. The output consists of individual swarm catalogues (`swarm_XXX.dat`), which are used in the multiscaling analysis. --- ### `multiscaling_swarms_objective_fits.py` Python program used to compute the multiscaling spectrum of each persistent swarm. The code performs: - computation of interevent-distance moments- estimation of scaling exponents- automatic selection of the optimal temporal scaling interval- estimation of the multiscaling spectrum- linear fitting for the negative-order branch- AICc-based polynomial fitting for the positive-order branch- production of all figures shown in the manuscript. --- ## Input data The codes require the relocated Southern California earthquake catalogue of Hauksson, E., Yang, W., & Shearer, P. (2012) which is publicly available from the Southern California Earthquake Data Center (SCEDC). The swarm-identification code produces the individual swarm catalogues used as input for the multiscaling analysis. --- ## Software requirements ### Fortran - GNU Fortran (gfortran ≥ 10 recommended) ### Python The Python analysis requires: - Python ≥ 3.10- NumPy- SciPy- Pandas- Matplotlib Additional packages may be required for figure production. --- ## Reproducibility Running the workflow in the following order reproduces the analyses presented in the paper: 1. Run `swarm_decl.f90` to identify persistent swarms.2. Run `multiscaling_swarms_objective_fits.py` using the generated swarm catalogues.3. The resulting scaling spectra and figures correspond to those presented in the manuscript. --- ## License This repository is released for academic and research purposes. Please cite the associated publication when using these codes.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.