Skip to main content
SpringerLink
Log in

Earthquake Networks as a Tool for Seismicity Investigation: a Review

  • Published:
Pure and Applied Geophysics Aims and scope Submit manuscript

Abstract

Seismic hazard assessment is one of the main targets of seismological research, aiming to contribute to reducing the catastrophic consequences of strong earthquakes (e.g., \( M \ge 6.0 \)). From the early stage of seismological research, both purely seismological and statistical methods were adopted for seismic hazard assessment. An approach towards this target was attempted by means of network theory, aiming to provide insight into the complex physical mechanisms that cause earthquakes and whether the occurrence of strong earthquakes can be predicted to some extent. Application of network theory in different areas of the world with intense seismic activity, such as Japan, California, Italy, Greece, Iran, and Chile, has yielded promising results that have negligible probability of being obtained by purely random guessing.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Abe, S., Pasten, D., Munoz, V., & Suzuki, N. (2011). Universalities of earthquake-network characteristics. Chinese Science Bulletin,56(34), 3697–3701.

    Google Scholar 

  • Abe, S., & Suzuki, N. (2004a). Small-world structure of earthquake network. Physica A: Statistical Mechanics and Its Applications,337, 357–362.

    Google Scholar 

  • Abe, S., & Suzuki, N. (2004b). Scale-free network of earthquakes. Europhysics Letters,65, 581–586.

    Google Scholar 

  • Abe, S., & Suzuki, N. (2006). Complex-network description of seismicity. Nonlinear Processes in Geophysics,13, 145–150.

    Google Scholar 

  • Abe, S., & Suzuki, N. (2009). Main shocks and evolution of complex earthquake networks. Brazilian Journal of Physics,39(2A), 428–430.

    Google Scholar 

  • Abe, S., & Suzuki, N. (2012). Universal law for waiting internal time in seismicity and its implication to earthquake network. Europhysics Letters,97(4), 1–21. https://doi.org/10.1209/0295-5075/97/49002.

    Article  Google Scholar 

  • Albert, R., & Barabasi, A. (2002). Statistical mechanics of complex networks. Reviews of Modern Physics,74, 47–97.

    Google Scholar 

  • Altınok, Y. (1991). Evaluation of earthquake risk in West Anatolia by semi-Markov model. Jeofizik,5, 135–140.

    Google Scholar 

  • Altınok, Y., & Kolçak, D. (1999). An application of the semi-Markov model for earthquake occurrences in North Anatolia, Turkey. Journal of the Balkan Geophysical Society (BGS),2, 90–99.

    Google Scholar 

  • Aydin, N., Duzgun, H., Wenzel, F., & Heinimann, H. (2017). Integration of stress testing with graph theory to assess the resilience of urban road networks under seismic hazards. Natural Hazards,91, 37–68.

    Google Scholar 

  • Baek, W., Lim, G., Kim, K., Chang, K., Jung, J., Seo, S., et al. (2011). Robustness of the topological properties of a seismic network. Journal of the Korean Physical Society,58(6), 1712–1714.

    Google Scholar 

  • Baiesi, M., & Paczuski, M. (2004). Scale-free networks of earthquakes and aftershocks. Physical Review,69(6), 066106.

    Google Scholar 

  • Baiesi, M., & Paczuski, M. (2005). Complex networks of earthquakes and aftershocks. Nonlinear Processes in Geophysics,12, 1–11.

    Google Scholar 

  • Bak, P., Christensen, K., Danon, L., & Scanlon, T. (2002). Unified scaling law for earthquakes. Physical Review Letters,88, 178501.

    Google Scholar 

  • Bak, P., & Tang, C. (1989). Earthquakes as a self-organized critical phenomenon. Journal of Geophysical Research,94(B11), 635–637.

    Google Scholar 

  • Barabasi, A., & Albert, R. (1999). Emergence of scaling in random networks. Science,286, 509–512.

    Google Scholar 

  • Belkacem, F., Zekri, N., & Terbeche, M. (2015). Statistical characterization of a small-world network applied to forest fires. Springer Proceedings in Mathematics and Statistics,128, 27–37.

    Google Scholar 

  • Bialonski, S., Horstmann, M., & Lehnertz, K. (2010). From brain to earth and climate systems: Small-world interaction networks or not? American Institute of Physics Chaos,20, 013134.

    Google Scholar 

  • Billio, M., Getmansky, M., Lo, A., & Pelizzon, L. (2012). Econometric measures of connectedness and systemic risk in the finance and insurance sectors. Journal of Financial Economics,104(3), 535–559.

    Google Scholar 

  • Bullmore, E., Fornito, A., & Zalesky, A. (2016). Fundamentals of brain network analysis (p. 494). Cambridge: Academic. (eBook ISBN: 9780124081185).

    Google Scholar 

  • Carbone, V., Sorriso-Valvo, L., Harabaglia, P., & Guerra, I. (2005). Unified scaling law for waiting times between seismic events. Europhysics Letters,71(6), 1036–1042.

    Google Scholar 

  • Chorozoglou, D., & Kugiumtzis, D. (2018). Testing the randomness of correlation networks from multivariate time series. Journal of Complex Networks. https://doi.org/10.1093/comnet/cny020.

    Article  Google Scholar 

  • Chorozoglou, D., Kugiumtzis, D., & Papadimitriou, E. (2017). Application of complex network theory to the recent foreshock sequences of Methoni (2008) and Kefalonia (2014) in Greece. Acta Geophysica,65(3), 543–553.

    Google Scholar 

  • Chorozoglou, D., Kugiumtzis, D., & Papadimitriou, E. (2018). Testing the structure of earthquake networks from multivariate time series of successive main shocks in Greece. Physica A Statistical Mechanics and Its Applications,499C, 28–39.

    Google Scholar 

  • Cornell, C. (1968). Engineering seismic risk analysis. Bulletin of the Seismological Society of America,58, 1583–1606.

    Google Scholar 

  • Corral, A. (2004). Long-term clustering, scaling, and universality in the temporal occurrence of earthquakes. Physical Review Letters,92, 108501.

    Google Scholar 

  • Daskalaki, E., Spiliotis, K., Siettos, C., Minadakis, G., & Papadopoulos, G. (2016). Foreshocks and short-term hazard assessment of large earthquakes using complex networks: the case of the 2009 L’Aquila earthquake. Nonlinear Processes in Geophysics,23, 241–256.

    Google Scholar 

  • Del Genio, C., Kim, H., Toroczkai, Z., & Bassler, K. (2010). Efficient and exact sampling of simple graphs with given arbitrary degree sequence. PLoS One,5(4), e10012.

    Google Scholar 

  • Donges, J., Zou, Y., Marwan, N., & Kurths, J. (2009). The backbone of the climate network export. EPL Europhysics Letters,87, 48007.

    Google Scholar 

  • Emmert-Streib, F., & Dehmer, M. (2010). Influence of the time scale on the construction of financial networks. PLoS One,5(9), e12884.

    Google Scholar 

  • Erdős, P., & Rényi, A. (1959). On random graphs. Pub. Math. (Debrecen),6, 290–297.

    Google Scholar 

  • Fiedor, P. (2014). Networks in financial markets based on the mutual information rate. Physical Review E,89, 052801.

    Google Scholar 

  • Girvan, M., & Newman, M. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences,99, 7821–7826.

    Google Scholar 

  • Gutenberg, B., & Richter, C. (1944). Frequency of earthquakes in California. Bulletin of the Seismological Society of America,34, 185–188.

    Google Scholar 

  • Heiberger, R. (2014). Stock network stability in times of crisis. Physica A: Statistical Mechanics and Its Applications,393, 376.

    Google Scholar 

  • Helmstetter, A., Kagan, Y., & Jackson, D. (2007). High-resolution time-independent grid-based forecast for m > 5 earthquakes in California. Seismological Research Letters,78(1), 78–86.

    Google Scholar 

  • Herrera, C., Nava, F., & Lomnitz, C. (2006). Time-dependent earthquake hazard evaluation in seismogenic systems using mixed Markov chains: an application to the Japan area. Earth Planets Space,58, 973–979.

    Google Scholar 

  • Hill, D., Reasenberg, P., Michael, A., Arabaz, W., Beroza, G., Brumbaugh, D., et al. (1993). Seismicity remotely triggered by the magnitude 7.3 Landers, California earthquake Science,260, 1617–1623.

    Google Scholar 

  • Hlinka, J., Hartman, D., & Palus, M. (2012). Small-world topology of functional connectivity in randomly connected dynamical systems, Chaos: an Interdisciplinary Journal of Nonlinear Sciences,22(3), 033107.

    Google Scholar 

  • Holliday, J., Chen, C., Tiampo, K., Rundle, J., Turcotte, D., & Donnellan, A. (2007). A RELM earthquake forecast based on pattern informatics. Seismological Research Letters,78(1), 87–93.

    Google Scholar 

  • Horvath, S. (2011). Weighted network analysis, applications in genomics and systems biology. New York: Springer.

    Google Scholar 

  • Janer, C., Biton, D., & Batac, R. (2017). Incorporating space, time, and magnitude measures in a network characterization of earthquake events. Acta Geophysica,65, 1153–1166.

    Google Scholar 

  • Jeong, H., Mason, S., Barabasi, A., & Oltvai, Z. (2001). Lethality and centrality in protein networks. Nature,411, 41.

    Google Scholar 

  • Jimenez, A., Tiampo, K., & Posadas, A. (2008). Small world in a seismic network: the California case. Nonlinear Processes in Geophysics,15, 389–395.

    Google Scholar 

  • Kagan, Y., & Jackson, D. (1994). Long-term probabilistic forecasting of earthquakes. Journal of Geophysical Research,99, 13685–13700.

    Google Scholar 

  • Kanamori, H., & Anderson, L. (1975). Theoretical basis of some empirical relations in seismology. Bulletin of the Seismological Society of America,65(5), 1073–1095.

    Google Scholar 

  • Kugiumtzis, D. (2002). Statistically transformed autoregressive process and surrogate data test for nonlinearity. Physical Review E,66, 025201.

    Google Scholar 

  • Kugiumtzis, D., & Kimiskidis, V. (2015). Direct causal networks for the study of transcranial magnetic stimulation effects on focal epileptiform discharges. International Journal of Neural Systems,25, 1550006.

    Google Scholar 

  • Kugiumtzis, D., Koutlis, C., Tsimpiris, A., & Kimiskidis, V. (2017). Dynamics of epileptiform discharges induced by transcranial magnetic stimulation in genetic generalized epilepsy. International Journal of Neural Systems,27(7), 1750037.

    Google Scholar 

  • Lennartz, S., Livina, V., Bunde, A., & Havlin, S. (2008). Long-term memory in earthquakes and the distribution of interoccurrence times. Europhysics Letters,81, 69001.

    Google Scholar 

  • León, D., Valdivia, J., & Bucheli, V. (2018). Modeling of Colombian seismicity as small-world networks. Seismological Research Letters,89(5), 1807–1816.

    Google Scholar 

  • Lippiello, E., Arcangelis, L., & Godano, C. (2008). Influence of time and space correlations on earthquake magnitude. Physical Review Letters,100, 038501.

    Google Scholar 

  • Livina, V., Havlin, S., & Bunde, A. (2005). Memory in the occurrence of earthquakes. Physical Review Letters,95, 208501.

    Google Scholar 

  • Lomnitz, C. (1974). Global tectonics and earthquake risk. Amsterdam: Elsevier.

    Google Scholar 

  • Maslov, S., & Sneppen, K. (2002). Specificity and stability in topology of protein networks. Science,296, 910–913.

    Google Scholar 

  • Milgram, S. (1967). The small-world problem. Psychology Today,1(1), 61–67.

    Google Scholar 

  • Molloy, M., & Reed, B. (1995). A critical point for random graphs with a given degree sequence. Random Structures and Algorithms,6(2–3), 161–180.

    Google Scholar 

  • Nava, F., Herrera, C., Frez, J., & Glowacka, E. (2005). Seismic hazard evaluation using Markov chains. Application to the Japan area. Pure and Applied Geophysics,162, 1347–1366.

    Google Scholar 

  • Newman, M. (2010). Networks, an introduction. Oxford: Oxford University Press.

    Google Scholar 

  • Omori, F. (1894). On the aftershocks of earthquakes. Journal of the College of Science, Imperial University of Tokyo,7, 111–120.

    Google Scholar 

  • Opsahl, T., Colizza, V., Panzarasa, P., & Ramasco, J. (2008). Prominence and control: The weighted rich-club effect. Physical Review Letters,101, 168702.

    Google Scholar 

  • Palus, M., Hartman, D., Hlinka, J., & Vejmelka, M. (2011). Discerning connectivity from dynamics in climate networks. Nonlinear Processes in Geophysics,18, 751–763.

    Google Scholar 

  • Papana, A., Kyrtsou, C., Kugiumtzis, D., & Diks, C. (2017). Financial networks based on Granger causality: A case study. Physica A: Statistical Mechanics and Its Applications,482, 65–73.

    Google Scholar 

  • Papo, D., Zanin, M., Martinez, J., & Buldu, J. (2016). Beware of the small-world neuroscientist. Frontiers in Human Neuroscience,10, 96.

    Google Scholar 

  • Pastén, D., Torres, F., Toledo, B., Muñoz, V., Rogan, J., & Valdivia, J. (2016). Time-based network analysis before and after the Mw 8.3 Illapel earthquake 2015 Chile. Pure and Applied Geophysics,173(7), 2267–2275.

    Google Scholar 

  • Porta, A., & Faes, L. (2016). Wiener-Granger causality in network physiology with applications to cardiovascular control and neuroscience. Proceedings of the IEEE,104, 282–309.

    Google Scholar 

  • Rhoades, D. (2007). Application of the EEPAS model to forecasting earthquakes of moderate magnitude in Southern California. Seismological Research Letters,78(1), 110–115.

    Google Scholar 

  • Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. Journal of Neuroscience,52, 1059–1069.

    Google Scholar 

  • Schreiber, T., & Schmitz, A. (1996). Improved surrogate data for nonlinearity tests. Physical Review Letters,77(4), 635–638.

    Google Scholar 

  • Steeples, W., & Steeples, D. (1996). Far-field aftershocks of the 1906 earthquake. Bulletin of the Seismological Society of America,86(4), 921–924.

    Google Scholar 

  • Tenenbaum, J., Havlin, S., & Stanley, H. (2012). Earthquake networks based on similar activity patterns. Physical Review E,86, 046107.

    Google Scholar 

  • Van den Heuvel, M., Stam, C., Boersma, M., & HulshoffPol, H. (2008). Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain. Journal of Neuroscience,43, 528–539.

    Google Scholar 

  • Votsi, I., Limnios, N., Tsaklidis, G., & Papadimitriou, E. (2012). Estimation of the expected number of earthquake occurrences based on semi-Markov models. Methodology and Computing in Applied Probability,14, 685–703.

    Google Scholar 

  • Votsi, I., Limnios, N., Tsaklidis, G., & Papadimitriou, E. (2013). Hidden Markov models revealing the stress field underlying the earthquake generation. Physica A: Statistical Mechanics and Its Applications,392, 2868–2885.

    Google Scholar 

  • Votsi, I., Limnios, N., Tsaklidis, G., & Papadimitriou, E. (2014). Hidden semi-Markov modeling for the estimation of earthquake occurrence rates. Communications in Statistics-Theory and Methods,43, 1484–1502.

    Google Scholar 

  • Wang, X., & Chen, G. (2003). Complex networks: Small-world, scale-free and beyond. Feature,3, 6–20.

    Google Scholar 

  • Wang, X., Koç, Y., Derrible, S., Ahmad, S., Pino, W., & Kooij, R. (2017). Multi-criteria robustness analysis of metro networks. Physica A: Statistical Mechanics and Its Applications,474, 19–31.

    Google Scholar 

  • Wanliss, J., Muñoz, V., Pastén, D., Toledo, B., & Valdivia, J. (2017). Critical behavior in earthquake energy dissipation. The European Physical Journal B,90, 167.

    Google Scholar 

  • Watts, D., & Strogatz, S. (1998). Collective dynamics of small-world networks. Nature,393, 440–442.

    Google Scholar 

  • Zhang, X., & Gan, C. (2018). Global attractivity and optimal dynamic countermeasure of a virus propagation model in complex networks. Physica A: Statistical Mechanics and Its Applications,490, 1004–1018.

    Google Scholar 

Download references

Acknowledgements

Financial support by the European Union and Greece (Partnership Agreement for the Development Framework 2014-2020) for the project “Development and application of time-dependent stochastic models in selected regions of Greece for assessing the seismic hazard” is gratefully acknowledged (MIS5004504). Geophysics Department Contribution 923.

Author information

Authors and Affiliations

  1. Department of Geophysics, School of Geology, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece

    D. Chorozoglou, A. Iliopoulos, C. Kourouklas, O. Mangira & E. Papadimitriou

Authors
  1. D. Chorozoglou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Iliopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. C. Kourouklas
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. O. Mangira
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. E. Papadimitriou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to D. Chorozoglou.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chorozoglou, D., Iliopoulos, A., Kourouklas, C. et al. Earthquake Networks as a Tool for Seismicity Investigation: a Review. Pure Appl. Geophys. 176, 4649–4660 (2019). https://doi.org/10.1007/s00024-019-02253-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00024-019-02253-w

Keywords

Search

Navigation