Variations in Probability Distributions as Indicators of Seismic Phases: A Case Study from Chile

In this study, we analyze the seismic activity of Chile, a region known for its diverse seismic characteristics, to validate and strengthen our findings on the identification of indicators of precursory phases. We employ Bayesian inference, processing data through sliding time windows. Each window contains a fixed number of events and shifts with each new event. Regarding the temporal variations in magnitude distribution, we observe similar patterns in seismic sequences in both Italy and Chile. The estimated q-index significantly decreases before strong earthquakes and increases sharply afterward, indicating its potential as a marker of the activation state of these systems. In addition, in analyzing the spatial distances between successive earthquakes we con-sider various distributions, such as tapered Pareto and generalized gamma. The optimal distribution for each time window is selected by comparing the estimated values of the posterior marginal likelihood. We discover that the best-fitting distribution changes over time, serving as an additional indicator of the activation state of the systems.