Scale-free networks of earthquakes and aftershocks.

We propose a metric to quantify correlations between earthquakes. The metric consists of a product involving the time interval and spatial distance between two events, as well as the magnitude of the first one. According to this metric, events typically are strongly correlated to only one or a few preceding ones. Thus a classification of events as foreshocks, main shocks, or aftershocks emerges automatically without imposing predetermined space-time windows. In the simplest network construction, each earthquake receives an incoming link from its most correlated predecessor. The number of aftershocks for any event, identified by its outgoing links, is found to be scale free with exponent gamma=2.0(1). The original Omori law with p=1 emerges as a robust feature of seismicity, holding up to years even for aftershock sequences initiated by intermediate magnitude events. The broad distribution of distances between earthquakes and their linked aftershocks suggests that aftershock collection with fixed space windows is not appropriate.

[1]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[2]  Proceedings of the Thirty-fifth Annual Meeting of the Geological Society of America, held at Ann Arbor, Michigan, Thursday–Saturday, December 28–30, 1922 , 1923 .

[3]  L. Knopoff,et al.  Is the sequence of earthquakes in Southern California, with aftershocks removed, Poissonian? , 1974, Bulletin of the Seismological Society of America.

[4]  L. Knopoff,et al.  Bursts of aftershocks, long-term precursors of strong earthquakes , 1980, Nature.

[5]  L. Knopoff,et al.  b Values for foreshocks and aftershocks in real and simulated earthquake sequences , 1982 .

[6]  S. Redner,et al.  Introduction To Percolation Theory , 2018 .

[7]  Takayuki Hirata,et al.  A correlation between the b value and the fractal dimension of earthquakes , 1989 .

[8]  P. Bak,et al.  Earthquakes as a self‐organized critical phenomenon , 1989 .

[9]  C. Frohlich,et al.  Single‐Link Cluster Analysis As A Method to Evaluate Spatial and Temporal Properties of Earthquake Catalogues , 1990 .

[10]  Yan Y. Kagan,et al.  Likelihood analysis of earthquake catalogues , 1991 .

[11]  D. Turcotte Fractals and Chaos in Geology and Geophysics , 1992 .

[12]  G. Molchan,et al.  Aftershock identification: methods and new approaches , 1992 .

[13]  W. Ellsworth,et al.  Seismicity Remotely Triggered by the Magnitude 7.3 Landers, California, Earthquake , 1993, Science.

[14]  J. Dieterich A constitutive law for rate of earthquake production and its application to earthquake clustering , 1994 .

[15]  Yan Y. Kagan,et al.  Observational evidence for earthquakes as a nonlinear dynamic process , 1994 .

[16]  Remo Guidieri Res , 1995, RES: Anthropology and Aesthetics.

[17]  Y. Ogata,et al.  The Centenary of the Omori Formula for a Decay Law of Aftershock Activity , 1995 .

[18]  Per Bak,et al.  How Nature Works , 1996 .

[19]  E. Bonabeau How nature works: The science of self-organized criticality (copernicus) , 1997 .

[20]  David M. Raup,et al.  How Nature Works: The Science of Self-Organized Criticality , 1997 .

[21]  Y. Ogata Space-Time Point-Process Models for Earthquake Occurrences , 1998 .

[22]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[23]  D. Turcotte,et al.  Self-organized criticality , 1999 .

[24]  Y. Ogata Seismicity Analysis through Point-process Modeling: A Review , 1999 .

[25]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[26]  Michalis Faloutsos,et al.  On power-law relationships of the Internet topology , 1999, SIGCOMM '99.

[27]  N. Beeler,et al.  On rate-state and Coulomb failure models , 2000 .

[28]  L. Knopoff,et al.  The magnitude distribution of declustered earthquakes in Southern California. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[29]  A. Wagner The yeast protein interaction network evolves rapidly and contains few redundant duplicate genes. , 2001, Molecular biology and evolution.

[30]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[31]  Kim Christensen,et al.  Unified scaling law for earthquakes. , 2001, Physical review letters.

[32]  K. Sneppen,et al.  Specificity and Stability in Topology of Protein Networks , 2002, Science.

[33]  D Sornette,et al.  Diffusion of epicenters of earthquake aftershocks, Omori's law, and generalized continuous-time random walk models. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[34]  Foreshocks explained by cascades of triggered seismicity , 2002, physics/0210130.

[35]  Funabashi,et al.  Scale-free network of earthquakes , 2002 .

[36]  K. McClements,et al.  Solar flares as cascades of reconnecting magnetic loops. , 2002, Physical review letters.

[37]  Alvaro Corral Local distributions and rate fluctuations in a unified scaling law for earthquakes. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[38]  Ericka Stricklin-Parker,et al.  Ann , 2005 .