Description of earthquake sequences using complex network theory: the cases of Italy (L'Aquila, 2009) and Southern California (Baja, 2010)

Complex networks pertain to the structure of many real-world systems influencing their dynamics. Earthquakes are a highly complex natural process that develops in the space-time-size domains given that the state of the seismogenic layer of the Earth is characterized by self-organized criticality. Over the last years, complex network theory was tested as a tool to quantify the topological characteristics of seismic activity aiming to investigate possible correlation patterns between earthquakes. With the aid of complex network theory, we have analyzed foreshock and aftershock sequences associated with the mainshocks of L'Aquila (Italy), 6th April 2009, Mw=6.3, and of Baja (Southern California) 4th April 2010, Mw=7.2. After testing the catalogues for data completeness on the basis of the magnitude-frequency relationship, we selected magnitude cut-off of 1.3 and 1.0, respectively. We constructed the underlying network that describes the evolution of the two sequences in space and extracted the statistical properties of the underlying topology resulting in characteristic scale-free and small-world structures. We found that the corresponding earthquake networks form a scale-free degree distribution and we computed their basic statistical measures, such as the Average Clustering Coefficient, Mean Path Length and Entropy. Taking into account a spatio-temporal sensitivity analysis, we found that the statistical measures of the two networks change considerably before and after the two main shocks, thus underlying the space-time clustering of the sequences. Our findings are in agreement with the ones obtained by using well established classical methods of statistical seismology. Thus, we believe that the proposed approach has the potential to serve as a supplementary or stand-alone methodology towards the better assessment of seismicity clusters