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Inter occurrence time statistics of successive large earthquakes: analyses of the global CMT dataset

Bantidi Thystere Matondo

Acta Geophysica

2022

Vol. 70, no 6

2603--2619

The purpose of this study is to discuss the statistical distributions of the inter-occurrence times of successive large earthquakes. We examine the Global Centroid Moment Tensor Catalog from 1976 to 2021 to analyze shallow earthquakes with a moment magnitude, Mw ≥ 7.0. After removing the aftershocks that occur in and around the faults of the mainshock within a given time–space window, we select the main events and search for successive ones in the space–time window to group them in clusters. We use four renewal models (Brownian passage time, gamma, lognormal, and Weibull) to fit the data. We estimate the models’ parameters using the maximum likelihood estimation method. Then, we perform two goodness-of-fit tests: the Akaike information criterion and the Kolmogorov–Smirnov test to evaluate the suitability of the model distributions to the observed data. The results reveal that the lognormal distribution provides the best fit to the observed data in at least 50% of the regions under consideration. An intermediate fit comes from the Weibull distribution, whereas the Brownian passage time and gamma distributions exhibit a poor fit. Then, we estimated the conditional probability of the occurrence of successive large earthquakes for the 10-year period between 2022 and 2032. Estimates range from 16 to 96%. To evaluate the usefulness of the interevent time-dependent earthquake modeling, we compared the results with the time-independent Poisson distribution. The results show that the renewal model, associated with a time-dependent earthquake hazard, is significantly better than a time-independent Poisson model.