GNGTS 2022 - Atti del 40° Convegno Nazionale

218 GNGTS 2022 Sessione 2.1 LOCAL EARTHQUAKE CONDITIONAL PROBABILITY BASED ON LONG TERM CO 2 MEASUREMENTS L. Pierotti 1 , C. Fidani 2 , G. Facca 1 , F. Gherardi 1 1 Istituto di Geoscienze e Georisorse CNR, Pisa, Italy 2 Central Italy Electromagnetic Network, Fermo, Italy A network of automatic stations equipped with sensors for the continuous monitoring of geochemical parameters (Geochemistry Network of Tuscany, GNT) has been designed and realized at IGG-CNR-Pisa in keeping with the recommendations of the International Association for Seismology and Physics of Earth’s Interior (IASPEI; Wyss, 1991; Wyss and Booth, 1997). The GNT is a part of a seismic prevention/prediction program, the Regional Government of Tuscany, Italy. It is operating since late 2002, and currently consists of six continuous automatic stations installed in the areas of highest seismic risk of Tuscany: Garfagnana, Lunigiana, Mugello, Upper Tiber Valley and Mt. Amiata. The main objective of the network is to study possible geochemical precursors to seismic activity (Cioni et al. , 2007). Specifically, temperature, pH, and electrical conductivity are measured using high precision portable instrumentation, while CO 2 is calibrated with standards of known concentration. The elaboration of long-term time series allowed for an accurate definition of the geochemical background, and for the recognition of a number of geochemical anomalies in concomitance with the most energetic seismic events occurred during the monitoring period (Pierotti et al. , 2015). In the Garfagnana region, the Gallicano thermo-mineral spring is currently monitored with sensors for the simultaneous measurement of temperature (T), pH, electrical conductivity (EC), redox potential (ORP) and dissolved content of CO 2 . The measurement of CO 2 dissolved concentration is done with an extraction cell specifically designed and realized in the IGG-CNR- Pisa laboratories (Cioni et al. , 2007). The station operates with flowing water (about 5 liters per minute), and a frequency acquisition of 1 s for all the parameters. Average, median and variance of the data are calculated over a period of 5 min and recorded with an in-situ data logger. Here, a first study of the possible correlation between regional seismic events and CO 2 anomalies is investigated between the beginning of April 2017 and the beginning of March 2021. Moreover, the correlations can be used to estimate the conditional probabilities of seismic events, so opening the possibility of an earthquake forecasting experiment using geochemical monitoring. Earthquake data were downloaded from ISIDe Working Group, ‘‘Italian Seismological Instrumental andparametric database’’, http://iside.rm.ingv.it ) toobtain a subset of ‘‘significant events’’ with magnitudes greater or equal to 1.0. The seismic dataset was declustered using zmap of MatLab, so to reduce the number of events to 339, see Figure 1. Earthquake locations were then compared with the Gallicano Station location, to consider only those seismic events which satisfy the relation between magnitude and distance equal to three Dobrovolsky radius: D = 3 10 0.43M (1) The remaining set was digitized considering the presence of a seismic event if the distance of seismic event epicentres from the Gallicano Station position D Gallicano ≤ D as “1” and the absence as “0”, for each day. The resulting time series was characterized by 1435 days, where 47 “1” indicated the main shocks occurred at an interaction distance from Gallicano. The CO 2 signal has been decomposed using the Census I method (Makridakis et al., 1998). According to Census technique, a time series (Xt) can be thought of as consisting of four different components: (1) seasonal (St), (2) trend (Tt), (3) cyclical (Ct), and (4) irregular (It) component. The difference between a cyclical and a seasonal component is that the latter occurs at regular (seasonal) intervals, while cyclical factors have usually a longer duration that varies from cycle to cycle. In the Census I method, the trend and cyclical components are combined into a single trend-