Probabilistic earthquake forecasting in Italy: bridging the gap between alarm-based and probability-based models

We present a probabilistic framework for evaluating earthquake forecasting models that use an alarm-based approach. In this approach, alarms are triggered by specific precursor signals. In a previous paper we compared such models and two ensemble models combining them in additive and multiplicative mode, with the ETAS (Epidemic Type Aftershock Sequence) forecasting model, which is defined in a probability-based approach, by making the latter to issue an alarm when the expected rate exceeds a pre-defined threshold. In this work we compare the alarm-based models with the ETAS and with another probability-based model, EEPAS (Every Earthquake a Precursor According to Scale) previously applied to Italy, using the testing procedures developed for probability-based models within the Collaboratory Study for Earthquake Predictability (CSEP) initiative. To do that, for the four alarm-based models, we compute empirical probabilities (frequencies) of Mw ≥ 5.0 earthquakes in Italy, inside and outside alarm time intervals issued by such models from 1990 to 2011. We then compare pseudo-prospectively the forecasting ability of all six models, by applying the CSEP tests on the time interval from 2012 to 2023. We found that the evaluation method used has a strong impact on the ranking of model performance. Probabilistic models like ETAS and EEPAS tend to score better under the CSEP testing framework whereas alarm-based models generally outperform probability-based ones when assessed using alarm-based metrics.