GNGTS 2024 - Atti del 42° Convegno Nazionale

Session 2.1 GNGTS 2024 different networks, we could use the two gains, G ∩ and GU, depending on whether we consider the observables distinct or indistinguishable in the intersection areas. The same parameter measured at different stations is combined as two different observations. Finally, G( ∩ ) increases for highly intercorrelated parameters and G(U) increases for slightly intercorrelated parameters, consequently forecasting probabilities are given. Fig. 2 – A potential scenario for testing a forecasting model in Italy involves leveraging the existing geophysical observational networks in the country In summary, the verification process for this model is applicable to any observable, whether from ground-based or space-based sources. It necessitates the availability of geophysical observation datasets covering sufficiently long common time intervals. After establishing the elementary time interval, the initial step involves identifying anomalous events in the observable or, alternatively, events of interest, and assigning "1"s in the respective series. Subsequently, the series are correlated pairwise to identify any peaks. If correlation peaks are observed, the probability gains attributed to individual observables can be determined. From these gains, an attempt can be made to evaluate the probability gains resulting from data fusion. The process can be resumed in Fig. 3. The author thank the Limadou Scienza + (ASI) Project for the financial support.