Resumen:
Earthquakes are one of the most notable natural phenomena that can be classified as complex systems.
These phenomena are unpredictable and potentially destructived can cause great human and economic losses. Seismological events are classified as complex systems due to a series of properties they exhibit. These characteristics manifest themselves, for example, in the form of long-range spatio-temporal correlations, power law distributions, the presence of criticality, and the emergence of non-linear patterns. In this project, seismological events will be studied from the point of view of Statistical Physics, especially through the use of complex network theory and Non-Extensive Statistical Mechanics (NSEM). As previous work has shown, these tools have proved effective in geophysical studies. Thus statistical analysis of global earthquake time series will be carried out in order to determine metrics that serve as criteria for creating correlation networks between seismological events.
Dynamic and topological parameters of the networks will be calculated in order to obtain not only how the earthquake series evolves, but also how they relate to each other. The results obtained for these networks will be compared with results using existing models in current literature, such as the successive connections model and the time window method. These results will also allow us to infer correlation properties between earthquake locations. In this way, we hope to contribute to a more complete understanding of the dynamics that govern seismological events, as well as their spatio-temporal correlations, highlighting the importance of a global analysis of events, and not just a local or regional one.
