LAUSR

Seismic clustering raises challenging questions concerning the nucleation process in regions marked by active faults. In this paper, we present a stochastic modeling approach to identify background and triggered seismicity in Northern Algeria. To perform the seismic clustering, we used the etasFLP package from the CRAN (Comprehensiv Archive Network) in R. The model was calibrated by testing and combining the FLP (Forward Likelihood Predictive) and ML(Maximum Likelihood) method for the non-parametric and the parametric parameters that describe the intensity function. On the whole, the results show that the greater contribution for events comes from the triggered earthquakes. The etasFLP package is suitable to describe the pattern for main shock and aftershocks sequences. Nevertheless, the discrepancies concern here the spatial distribution of the triggered events. In fact, the present modeling indicates the necessity to add, during the computation, additional terms for coefficients that represent external factors that influence the neighboring stress and would cause triggered events. Moreover, a database with more events could provide an accurate modeling that would represent the distribution between the background and the triggered events. The Northern Algeria is the location for diverse source of tremors and therefore, there is a need to identify clearly man-made activities that might trigger seismic sequence. The present work aims at proposing the application of the ETAS model for such purpose.