Memory and renewal aging of strong earthquakes in Hellenic seismicity. (February 2020) - Iliopoulos, A.; Chorozoglou, D.; Kourouklas, C.; Mangira, O.; Papadimitriou, E.
Highlights: Application of an updated Renewal Aging Algorithm on strong earthquake waiting times. Renewal Aging analysis of waiting time windows corresponding to both short and intermediate-term forecasting. Different waiting time (short, intermediate) windows correspond to different (Poisson, non-Poisson) stochastic processes. Evidence for hidden memory between strong earthquakes. The statistics of waiting times can be a superposition of different (Poisson, non-Poisson) stochastic processes. Abstract: The complex turbulent dynamics of seismogenesis in the area of Greece is investigated, by applying an alternative approach based on renewal theory, specifically the Renewal Aging Algorithm, to two earthquake interevent (waiting) time series. The data sets were extracted from the regional earthquake catalogue compiled from the Geophysics Department, Aristotle University of Thessaloniki and concern: 576 earthquakes of M ≥5.5, during 1911–2017 and 113 earthquakes of M≥6.5, in the period of 1845–2017. The methodology is efficient for studying persistency and/or intermittent structures in different time scales of Hellenic seismogenesis by qualitative estimating the amount of memory, corresponding to the ratio between Poisson events and non-Poisson critical events. For the application of the Renewal Aging Algorithm time scales corresponding to short and intermediate-term forecasting were considered, namely 2 and 3 weeks to several years corresponding to the maximum Waiting Time for each data set. The results of the statistical analysis reveal transitions from time-homogeneous Poisson to time-homogeneous non-Poisson dynamics and non-homogeneous non-Poisson dynamics, starting from short time scales and going to longer time scales. These results can shed more light to the concept of the seismic cycle hypothesis and to the generation of time-dependent stochastic modeling. It has to be mentioned that these time series are relatively short and therefore additional statistical analysis is required to verify the aforementioned findings.