GNGTS 2024 - Atti del 42° Convegno Nazionale

Session 1.1 GNGTS 2024 Linking Seismicity, Geochemical And Environmental Data: The New Fronter Of Multparametric Networks E. Ferrari 1 , M. Massa 1 , A.L. Rizzo 2,1 , S. Lovat 1 , F. Di Michele 1 1 Natonal Insttute of Geophysics and Volcanology (INGV), Milano, Italy 2 Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milano, Italy Nowadays increasingly consciousness on the interacton between tectonics and crustal fuids dynamics is lacking simultaneous monitoring of the relatve key factors. Changes in water chemistry and levels, spring discharges, soil fux regimes (e.g., CO 2 , CH 4 , radon) and compositons of dissolved gases in water are well documented in the literature (e.g., Italiano et al., 2001, 2004; Wang and Manga, 2021; Chiodini et al., 2020; Gori and Barberio, 2022 and references therein), as being pre-, co- and post-seismic modifcatons as well as being markers of the local tectonic stress actng in the crust. However, geological diferences among sites require specifc knowledge of crustal fuids response to seismicity. For this purpose, multparametric statons have been set up startng from the end of 2021 and equipped with: (i) sensors installed in water wells measuring water level, temperature, and electrical conductvity; (ii) meteorological sensors measuring atmospheric pressure, temperature, rain, humidity, wind speed and directon; (iii) seismic sensors providing accelerometric and velocimetric datasets; (iv) radon sensors; (v) CO 2 soil fux chamber. Statons are placed on the major seismogenic structures and are widely distributed among the Alps, Apennines and Pianura Padana. Our new multparametric network is aimed to address this point and, to the best of our knowledge, it is the frst network developed in Italy under this philosophy. Data are transmited in near real-tme to an ad hoc developed dynamic relatonal database and displayed in a dedicated website. The built-in philosophy is to easily compare distnct parameters from the various sensors and possibly recognize cause-efect relatonships among them. A statstc approach is also applied to the tme-series to investgate intra-annual and inter-annual trends and correlatons among diferent parameters. Alternatve methods (e.g., signal decompositon, spike detecton) will be presented and discussed.