The variation in Coulomb failure stress (CFS) plays a crucial role in either increasing or decreasing seismic activity. In cases in which the standard epidemic-type aftershock sequence (ETAS) model does not adequately fit seismicity data, the potential deviations from empirical laws are explored. These deviations may arise from stress changes imparted by aseismic transients that lead swarm-like earthquake sequences to occur. The time-dependent background rate of seismicity serves as an indicator for detecting changes in CFS or the presence of transient aseismic forcing. We investigate seismic anomalies in the slow deforming Mt. Pollino, Italy seismogenic area, where a 4-yr-long swarm-like sequence partially filled a previously hypothesized seismic gap. The primary process of this seismic swarm is still under debate. Employing a nonstationary ETAS model on a new template-matching high-resolution catalog, we suggest a slow-slip event and fluid interplay as the main aseismic forces in triggering and developing this swarm-like sequence.

The existence of magnitude dependence in earthquake triggering has been reported. Such a correlation is linked to the issue of seismic predictability and it remains under intense debate whether it is physical or is caused by incomplete data due to the missing short-term aftershocks. Working firstly with a synthetic catalog generated by a numerical model that captures most statistical features of earthquakes and then with a high-resolution earthquake catalog for the Amatrice-Norcia (2016) sequence in Italy, where for the latter case we employ the stochastic declustering method to reconstruct the family tree among seismic events and limit our analysis to events above the magnitude of completeness, we found that the hypothesis of magnitude correlation can be rejected.

The epidemic type aftershock sequence (ETAS) model is used as a baseline model both for earthquake clustering and earthquake prediction. In most forecast experiments, the ETAS parameters are estimated based on a short and local catalog, therefore the model parameter optimization carried out by means of a maximum likelihood estimation may be not as robust as expected. We use Bayesian forecast techniques to solve this problem, where non-informative flat prior distributions of the parameters is adopted to perform forecast experiments on 3 mainshocks occurred in Southern California. A Metropolis–Hastings algorithm is employed to sample the model parameters and earthquake events. We also show, through forecast experiments, how the Bayesian inference allows to obtain a probabilistic forecast, differently from one obtained via MLE. Graphical Abstract

The Epidemic Type Aftershock Sequence (ETAS) model is a widely used tool for cluster analysis and forecasting, owing to its ability to accurately predict aftershock occurrences. However, its capacity to explain the increase in seismic activity prior to large earthquakes—known as foreshocks—has been called into question due to inconsistencies between simulated and experimental catalogs. To address this issue, we introduce a generalization of the ETAS model, called the Epidemic Type Aftershock Foreshock Sequence (ETAFS) model. This model has been shown to accurately describe seismicity in Southern California. In this study, we demonstrate that the ETAFS model is also effective in the Italian catalog, providing good agreement with the instrumental Italian catalogue (ISIDE) in terms of not only the number of aftershocks, but also the number of foreshocks—where the ETAS model fails. These findings suggest that foreshocks cannot be solely explained by cascades of triggered events, but can be reasonably considered as precursory phenomena reflecting the nucleation process of the main event.

Among the most important questions that await an answer in seismology, perhaps one is whether there is a correlation between the magnitudes of two successive seismic events. The answer to this question is considered of fundamental importance given the potential effect in forecasting models, such as Epidemic Type Aftershock Sequence models. After a meta-analysis of 29 papers, we speculate that given the lack of studies carried out with realistic physical models and given the possible bias due to the lack of events recorded in the experimental seismic catalogs, important improvements are necessary on both fronts to be sure to provide a statistically relevant answer.

A very efficient method for estimating the completeness magnitude mc and the scaling parameter b of earthquake magnitude distribution has been thoroughly tested using synthetic seismic catalogues. Subsequently, the method was employed to assess the capability of the b-value in differentiating between foreshocks and aftershocks, confirming previous findings regarding the Amatrice-Norcia earthquake sequence. However, a blind algorithm reveals that the discriminative ability of the b-value necessitates a meticulous selection of the catalogue, thereby reducing the predictability of large events occurring subsequent to a prior major earthquake.