Understanding how volcanic systems evolve over time is a major challenge due to their complex behaviour and constantly changing conditions. This study explores a novel approach to detecting significant changes in multiparametric signals of volcanic unrest by analysing how different types of data, such as ground deformation, gas emissions, temperature, and earthquakes, interact with each other. Focusing on the Solfatara–Pisciarelli volcano system, which is a more active area in the Campi Flegrei Caldera (Southern Italy), we used two advanced methods to identify critical transitions in the system: one to model the nonlinear relationships between variables, and the other to detect key moments when the system’s behaviour shifts. By including time delays between signals (LAG), we found that our model became much more accurate in identifying these changes. In contrast, models that ignored time lags showed higher uncertainty. The results highlight the importance and effectiveness of using integrated multivariate approaches such as Multivariable Fractional Polynomial Analysis (MFPA) and Global Critical Point Analysis (GCPA) to gain deeper insights into the systemic behaviour of the caldera and its temporal evolution within a complex area like the Campi Flegrei over the selected time period.
Vitale et al. (Sun,) studied this question.