Methods for Representing Earthquake Time Series with Networks
DOI:
https://doi.org/10.31449/inf.v48i14.5176Abstract
An earthquake is a natural phenomenon that occurs as a result of the internal dynamics of the Earth. It originates deep below the surface of the planet and cannot be predicted with our current knowledge. In this paper, we use a network analysis approach to analyze the characteristics and evolution of seismic activity over time. We implement several network models based on temporal and spatial interactions between earthquakes and on the assumption of self-similarity of seismic activity in selected geographic areas. We create sequences of networks generated in consecutive time windows and compare the networks between different models and time intervals. Additionally, we calculate a set of network structural characteristics and study their changes over time. The analysis shows that most models produce networks with such a structure that changes consistently with the intensity of seismic activity. Thus, based on the structural changes of networks, we can reliably identify the time windows with increased seismic activity.References
S. Seth, M. Wysession, An introduction to seismology, earthquakes, and earth structure, Blackwell, 2003, Ch. 3, p.
The Seismology and Natural Hazards Divisions of the European Geosciences Union (EGU), https://www.egu.eu/sm/can-we-predict-earthquakes/ (Visited: 25. 7. 2020).
Uprava Republike Slovenije za zaščito in reševanje, Izpostava Koper, Ocena potresne ogroženosti obalne regije, Tech. rep. (2019).
Y. Y. Kagan, D. D. Jackson, Probabilistic forecasting of earthquakes, Geophysical Journal International 143 (2) (2000) 438–453. doi:10.1046/j.1365-246X.2000.01267.x.
S. Rezaei, A. Darooneh, N. Lotfi, N. Asaadi, The earthquakes network: Retrieving the empirical seismological laws, Physica A: Statistical Mechanics and its Applications 471 (2017) 80–87. doi:10.1016/j.physa.2016.12.003.
S. Abe, N. Suzuki, Scale-Free Network of Earthquakes, Europhysics Letters 65 (4) (2004) 581–586. doi:10.1209/epl/i2003-10108-1.
X. He, H. Zhao, W. Cai, Z. Liu, S.-Z. Si, Earthquake networks based on space–time influence domain, Physica A: Statistical Mechanics and its Applications 407 (2014) 175–184. doi:10.1016/j.physa.2014.03.093.
M. Baiesi, M. Paczuski, Scale-free networks of earthquakes and aftershocks, Phys. Rev. E 69 (Jun 2004). doi:10.1103/PhysRevE.69.066106.
L. Telesca, M. Lovallo, Analysis of seismic sequences by using the method of visibility graph, Europhysics Letters 97 (02 2012). doi:10.1209/0295-5075/97/50002.
J. N. Tenenbaum, S. Havlin, H. E. Stanley, Earthquake networks based on similar activity patterns, Phys. Rev. E 86 (Oct 2012). doi:10.1103/PhysRevE.86.046107.
N. Lotfi, A. H. Darooneh, F. A. Rodrigues, Centrality in earthquake multiplex networks, Chaos 28 (June 2018). doi:10.1063/1.5001469.
S. Abe, N. Suzuki, Dynamical evolution of clustering in complex network of earthquake, The European Physical Journal B - Condensed Matter and Complex Systems 59 (2007) 93–97. doi:10.1140/epjb/e2007-00259-3.
High Sensitivity Seismograph Network Laboratory, http://www.hinet.bosai.go.jp/topics/JUICE/ (Visited: 1. 6. 2020).
Insitituto nazionale di geofisica e vulcanologia, http://iside.rm.ingv.it/ (Visited: 1. 6. 2020).
Northern California Earthquake Data Center, https://www.ncedc.org/ncedc/catalog-search.html (Visited: 1. 6. 2020).
Southern California Earthquake Data Center, https://scedc.caltech.edu/eq-catalogs/ (Visited: 1. 6. 2020).
C. Godano, E. Lippiello, L. de Arcangelis, Variability of the b value in the Gutenberg–Richter distribution, Geophysical Journal International 199 (3) (2014) 1765–1771. doi:10.1093/gji/ggu359.
J. Wang, D. Huang, S.-C. Chang, Y. Wu, New Evidence and Perspective to the Poisson Process and Earthquake Temporal Distribution from 55,000 Events around Taiwan since 1900, Natural Hazards Review 15 (2014) 38–47. doi:10.1061/(ASCE)NH.1527- 6996.0000110.
J. Greenhough, I. Main, A Poisson model for earthquake frequency uncertainties in seismic hazard analysis, Geophysical Research Letters 35 (07 2008). doi:10.1029/2008GL035353.
S. Abe, N. Suzuki, Main shocks and evolution of complex earthquake networks, Brazilian Journal of Physics 39 (2009) 428–430.
D. W. Steeples, D. D. Steeples, Farfield aftershocks of the 1906 earthquake, Bulletin of the Seismological Society of America 86 (4) (1996) 921–924. arXiv:https://pubs.geoscienceworld.org/bssa/article-pdf/86/4/921/2708582/BSSA0860040921.pdf.
J. K. Gardner, L. Knopoff, Is the sequence of earthquakes in southern california, with aftershocks removed, poissonian?, (1974).
M. Suzuki, A three dimensional box counting method for measuring fractal dimensions of 3d models, Proceedings of the 11th IASTED International Conference on Internet and Multimedia Systems and Applications, IMSA 2007 (01 2007).
P. Laurienti, K. Joyce, Q. Telesford, J. Burdette, S. Hayasaka, Universal fractal scaling of self-organized networks, Nature Precedings 5 (09 2010). doi:10.1038/npre.2010.4894.1.
M. Mukaka, Statistics corner: A guide to appropriate use of correlation coefficient in medical research, Malawi medical journal: the journal of Medical Association of Malawi 24 (2012) 69–71.
M. Bastian, S. Heymann, M. Jacomy, Gephi: An open source software for exploring and manipulating networks (2009). URL http://www.aaai.org/ocs/index.php/ICWSM/09/paper/view/154
F. Dragan, M. Habib, L. Viennot, Revisiting radius, diameter, and all eccentricity computation in graphs through certificates (03 2018).
V. Blondel, J.-L. Guillaume, R. Lambiotte, E. Lefebvre, Fast unfolding of communities in large networks, Journal of Statistical Mechanics Theory and Experiment 2008 (04 2008). doi:10.1088/1742-5468/2008/10/P10008.
R. van der Hofstad, Configuration Model, Vol. 1 of Cambridge Series in Statistical and Probabilistic Mathematics, Cambridge University Press, 2016, p. 216–255. doi:10.1017/9781316779422.010.
V. Traag, L. Waltman, N. J. van Eck, From louvain to leiden: guaranteeing well-connected communities, Scientific Reports 9 (2019) 5233. doi:10.1038/s41598-019-41695-z.
N. Laptev, J. Yosinski, L. E. Li, S. Smyl, Time-series extreme event forecasting with neural networks at uber, in: International Conference on Machine Learning, no. 34, (2017), pp. 1–5.
Downloads
Published
How to Cite
Issue
Section
License
I assign to Informatica, An International Journal of Computing and Informatics ("Journal") the copyright in the manuscript identified above and any additional material (figures, tables, illustrations, software or other information intended for publication) submitted as part of or as a supplement to the manuscript ("Paper") in all forms and media throughout the world, in all languages, for the full term of copyright, effective when and if the article is accepted for publication. This transfer includes the right to reproduce and/or to distribute the Paper to other journals or digital libraries in electronic and online forms and systems.
I understand that I retain the rights to use the pre-prints, off-prints, accepted manuscript and published journal Paper for personal use, scholarly purposes and internal institutional use.
In certain cases, I can ask for retaining the publishing rights of the Paper. The Journal can permit or deny the request for publishing rights, to which I fully agree.
I declare that the submitted Paper is original, has been written by the stated authors and has not been published elsewhere nor is currently being considered for publication by any other journal and will not be submitted for such review while under review by this Journal. The Paper contains no material that violates proprietary rights of any other person or entity. I have obtained written permission from copyright owners for any excerpts from copyrighted works that are included and have credited the sources in my article. I have informed the co-author(s) of the terms of this publishing agreement.
Copyright © Slovenian Society Informatika