Spatiotemporal analysis of the background seismicity in Northern Algeria

Abstract

This work has a twofold objective: to analyze for the first time the background seismicity of Northern Algeria and its vicinity and to apply a variety of statistical methods to identify its spatiotemporal features (Benali et al., Axioms, 2023). The earthquake catalog of Northern Algeria and its vicinity includes events from 1950 to 2021 within the region between latitudes 32° and 38° and longitudes -2° and 10°. Based on the investigation of the frequency-magnitude for the overall catalog, the magnitude threshold for completeness is set at 3.7, so that the analyzed dataset includes 1561 earthquakes. The considered area comprises the largest and the most damaging earthquakes ever recorded in the Mediterranean region: the M7.3 earthquake at El Asnam in 1980, the M6.9 earthquake at Boumerdes in 2003, and the M6.7 earthquake at El Asnam in 1954. The dataset is declustered by applying different methods, namely: the Gardner and Knopoff, Gruenthal, Uhrhammer, Reasenberg, Nearest Neighbour, and Stochastic Declustering methods. Each method identifies a different declustered catalog, that is a different subset of the earthquake catalog that represents the background seismicity, which is usually expected to be a realisation of a homogeneous Poisson process over time. A variety of statistical methods is applied to assess whether the background seismicity identified by each declustering method has the spatiotemporal properties typical of such a Poisson process. The main statistical tools of the analysis are the coefficient of variation, the Allan factor, the Markov-modulated Poisson process (also named switched Poisson process with multiple states), the Morisita index, and the L-function. Summing up our findings, all declustering methods reduce the time of correlation structures in the background seismicity at small timescales, but temporal correlation still remains at intermediate and higher timescale ranges. In particular, Gruenthal and Stochastic Declustering methods turn out the most effective in removing the time clustering structures from this catalog. The spatial clustering structure is also significantly reduced, but it is not eliminated from the declustered catalogs, due to the natural clustering of seismicity along active fault systems.