GNGTS 2023 - Atti del 41° Convegno Nazionale

Session 2.1 GNGTS 2023 between -20 and +20 days, which means CO 2 anomalies occurring from 20 days before to 20 days after the earthquakes. In analogy with past works, Figure 2 highlights three peaks of correlation between seismic and CO 2 anomaly events in correspondence with time differences ( Δ t) of -11, -1, and 0 days. This result was only partially in agreement with the -2 days correlation peak retrieved for the period 2017-2021 Pierotti et al., 2022), possibly suggesting some additional a phenomenon that anticipates small earthquakes. It is perhaps related to the magnitude of events, from moderate to strong, that occurred in 2010-2012, which included an M = 4.8 near Gallicano, several M5+ events under the Apennines, and the Modena earthquakes. It follows that a different methodology would be applied to consider anomalies from these strong and near-to-the-monitoring station events. A correlation histogram was also created between rain events and CO 2 anomalies, to assess the possible impact of precipitations on variations of CO 2 signal, as widely speculated in the literature. Rain events (ER) were considered significant (ER=1) when the daily rainfall overcome the arbitrary threshold of 10 mm (ER=1). On this ground, an additional histogram based on the coincidence between events of one rainfall with one CO 2 anomaly, ∑ {ER;ECO2} (ER × ECO 2 ), was derived (Figure 3). A peak of correlation is observed for a time difference values ( Δ t) of +1 days. Being Δ t the time difference T Rain – T CO2 , we can speculate that precipitation peaks at Gallicano can prevalently occur one day before CO 2 anomalies. This is another fundamental information that can help deciphering possible correlations between CO 2 anomalies and seismic events. Fig. 3. Cross-correlation values R between CO 2 anomalies and rainfalls in the range Δ t from -20 to +20 days. A pronounced peak occurs at +1 days, which means that rainfalls prevalently anticipate CO 2 anomalies of one day. References Box, G.E.P., Jenkins, G.M.;1976: Time Series Analysis: Forecasting and Control . Holden-Day, San Francisco. Cioni, R., Guidi, M., Pierotti, L., Scozzari, A.; 2007: An automatic monitoring network installed in Tuscany (Italy) for studying possible geochemical precursory phenomena . Nat. Hazards Earth Syst. Sci. 7 , 405–416.