Statistical properties of complex network for seismicity using depth-incorporated influence radius

• Autorzy: He Xuan , Wang Luyang , Zhu Hongbo , Liu Zheng

Identyfikatory

DOI

10.1007/s11600-019-00369-0

• Języki publikacji: EN

Abstrakty

EN

In recent years, seismic time series has been used to construct complex network models in order to describe the seismic complexity. The effect of the factor focal depth has been elided in some of these models. In this paper, we aim to construct a new complex network model for seismicity by considering depth factor from the earthquake catalog and investigate the statistical properties of the network. Since the networks have been proved to be scale-free and small-world properties, the new network models should be studied whether the properties have changed. The results show that the new network model by considering depth factor is still scale-free and small-world. However, it is found that its average degree is smaller than the original network. The clustering coefficient increases at the year including mainshocks. The assortativity coefficient, which demonstrates preferential attachment of nodes, is positive and shows consistent pattern when main shocks occur.

Słowa kluczowe

EN

complex network; seismicity; focal depth; assortative mixing

PL

sieć złożona; sejsmiczność; ogniskowa

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2019
• Tom: Vol. 67, no. 6
• Strony: 1515--1523
• Opis fizyczny: Bibliogr. 45 poz.

Twórcy

autor

He Xuan

• School of Sino‑Dutch Biomedical and Information Engineering, Northeastern University, Shenyang 110169, China
• Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang 110169, China

autor

Wang Luyang

• School of Computer Science and Engineering, Northeastern University, Shenyang, 110169, China

autor

Zhu Hongbo

• School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China

autor

Liu Zheng

• School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China

Bibliografia

• 1. Abe S, Suzuki N (2004a) Scale-free network of earthquakes. EPL (Europhys Lett) 65(4):581
• 2. Abe S, Suzuki N (2004b) Small-world structure of earthquake network. Phys A Stat Mech Appl 337(1–2):357–362
• 3. Abe S, Suzuki N (2006) Complex earthquake networks: hierarchical organization and assortative mixing. Phys Rev E 74(2):026113
• 4. Abe S, Suzuki N (2009a) Determination of the scale of coarse graining in earthquake networks. EPL (Europhys Lett) 87(4):48008
• 5. Abe S, Suzuki N (2009b) Scaling relation for earthquake networks. Phys A Stat Mech Appl 388(12):2511–2514
• 6. Abe S, Pastén D, Muñoz V, Suzuki N (2011) Universalities of earthquake-network characteristics. Chin Sci Bull 56(34):3697–3701
• 7. Abe S, Suzuki N (2012a) Aftershocks in modern perspectives: complex earthquake network, aging, and non-markovianity. Acta Geophys 60(3):547–561
• 8. Abe S, Suzuki N (2012b) Universal law for waiting internal time in seismicity and its implication to earthquake network. EPL (Europhys Lett) 97(4):49002
• 9. Albert R, Barabási AL (2002) Statistical mechanics of complex networks. Rev Mod Phys 74(1):47
• 10. Albert R, Jeong H, Barabási AL (1999) Diameter of the world-wide web. Nature 401(6749):130–131
• 11. Baiesi M, Paczuski M (2004) Scale-free networks of earthquakes and aftershocks. Phys Rev E 69(6):066106
• 12. Bak P, Christensen K, Danon L, Scanlon T (2002) Unified scaling law for earthquakes. Phys Rev Lett 88(17):178501
• 13. Båth M, Duda SJ (1963) Strain release in relation to focal depth. Geofis Pura E Appl 56(1):93–100
• 14. Borgatti SP, Mehra A, Brass DJ, Labianca G (2009) Network analysis in the social sciences. Science 323(5916):892–895
• 15. Chorozoglou D, Papadimitriou E, Kugiumtzis D (2019) Investigating small-world and scale-free structure of earthquake networks in Greece. Chaos Solitons Fractals 122:143–152
• 16. Crucitti P, Latora V, Marchiori M, Rapisarda A (2004) Error and attack tolerance of complex networks. Phys A Stat Mech Appl 340(1–3):388–394
• 17. Daoyi X (2001) The network features of large earthquake occurrence and some words on the debate of earthquake prediction. Earth Sci Front 8(2):211–216
• 18. Dorogovtsev SN, Goltsev AV, Mendes JFF (2006) K-core organization of complex networks. Phys Rev Lett 96(4):040601
• 19. Ferreira DS, Ribeiro J, Papa AR, Menezes R (2014) Towards evidences of long-range correlations in seismic activity. Physics, arXiv preprint arXiv:1405.0307
• 20. Gardner LK, Knopoff L (1974) Is the sequence of earthquakes in southern California, with aftershocks removed, poissonian. Bull Seismol Soc Am 64(5):1363–1367
• 21. Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99(12):7821–7826
• 22. Gutenberg B, Richter CF (1936) Magnitude and energy of earthquakes. Science 83(2147):183–185
• 23. Gutenberg B, Richter CF (1941) Seismicity of the earth. Geological Society of America No. 34: 1–126
• 24. Gutenberg B (1956) The energy of earthquakes. Q J Geol Soc 112(1–4):1–14
• 25. He X, Zhao H, Cai W, Liu Z, Si SZ (2014) Earthquake networks based on space–time influence domain. Phys A Stat Mech Appl 407:175–184
• 26. He X, Zhao H, Cai W, Li GG, Pei FD (2015) Analyzing the structure of earthquake network by k-core decomposition. Phys A Stat Mech Appl 421:34–43
• 27. Jeong H, Mason SP, Barabási AL, Oltvai ZN (2001) Lethality and centrality in protein networks. Nature 411(6833):41–42
• 28. Lotfi N, Darooneh AH (2012) The earthquakes network: the role of cell size. Eur Phys J B 85(1):23
• 29. Lotfi N, Darooneh AH, Rodrigues FA (2018) Centrality in earthquake multiplex networks. Chaos Interdiscip J Nonlinear Sci 28(6):063113
• 30. Manighetti I, Campillo M, Sammis C, Mai PM, King G (2005) Evidence for self-similar, triangular slip distributions on earthquakes: implications for earthquake and fault mechanics. J Geophys Res Solid Earth 110:B05302
• 31. Newman MEJ (2002) Assortative mixing in networks. Phys Rev Lett 89(20):208701
• 32. Newman MEJ (2003) Mixing patterns in networks. Phys Rev E 67(2):026126
• 33. Omori F (1895) On the after-shocks of earthquakes. J College Sci Imp Univ Japan 7:111–200
• 34. Persh SE, Houston H (2004) Strongly depth-dependent aftershock production in deep earthquakes. Bull Seismol Soc Am 94(5):1808–1816
• 35. Rezaei S, Moghaddasi H, Darooneh AH (2018) Preferential attachment in evolutionary earthquake networks. Phys A Stat Mech Appl 495:172–179
• 36. Rezaei S, Moghaddasi H, Darooneh AH, Zare M (2019a) Forecasting earthquakes by hybrid model of pattern informatic and pagerank methods. Bull Seismol Soc Am. https://doi.org/10.1785/0120180346
• 37. Rezaei S, Moghaddasi H, Darooneh AH (2019b) PageRank: an alarming index of probable earthquake occurrence. Chaos Interdiscip J Nonlinear Sci 29(6):063114
• 38. Stein RS (1999) The role of stress transfer in earthquake occurrence. Nature 402(6762):605
• 39. Vespignani A (2003) Evolution thinks modular. Nat Genet 35(2):118
• 40. Virkar Y, Clauset A (2014) Power-law distributions in binned empirical data. Ann Appl Stat 8(1):89–119
• 41. Wang X, Liu PLF (2006) An analysis of 2004 Sumatra earthquake fault plane mechanisms and Indian ocean tsunami. J Hydraul Res 44(2):147–154
• 42. Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393(6684):440
• 43. Xie ZM (2011) Network topology and network dynamical behavior of seismicity. Technol Earthq Disaster Prev 6(1):1–17
• 44. Zhang Y, Zhao H, He X, Pei FD, Li GG (2016) Bayesian prediction of earthquake network based on space–time influence domain. Phys A Stat Mech Appl 445:138–149
• 45. Zhou S, Mondragón RJ (2004) Accurately modeling the internet topology. Phys Rev E 70(6):066108

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).