Detecting CO2 anomalies in a spring on Mt. Amiata volcano (Italy). (April 2017) - Pierotti, L.; Gherardi, F.; Facca, G.; Piccardi, L.; Moratti, G.
Abstract: Located at the south-western slope of the Mt. Amiata volcano (Tuscany, Central Italy), the Bagnore spring has been investigated for geochemical precursors of earthquakes from 2004 to 2015. Over this period, several parameters of the spring have been monitored by discrete and continuous sampling. An automatic continuous monitoring station, equipped with sensors for the measurement of temperature, pH, redox potential, electrical conductivity, CO2 and CH4 dissolved concentration, is transmitting the registered signals to the remote server in Pisa 150 km northwest of Mt. Amiata. The Bagnore spring drains a shallow, short-circuiting aquifer hosted in the volcanic rocks of the volcano. Its emergence point is located in proximity of the intersection of two major fault systems that are supposed to provide a preferential ascent path to hydrothermal gases, mainly represented by C O 2 ( g ) and H 2 S ( g ), locally rising from depth. The most evident change occurred in the registered signals over the period is represented by the sudden increase in CO2 concentration measured starting from April 2010. Along with this increase in CO2, a slight increase in water temperature and in S O 4 concentration, associated to a decrease in pH, was also recorded. This trend has been interpreted as an evidence for the augmented inflow of deep gases into the shallow aquifer. The CO2 continuous signal recorded by the Bagnore automatic station has been then processed by applying multiple statistical techniques (i.e. artificial neural network analysis and Census I method) in the search for anomalies possibly related to local seismic activity. Anomalous signals have been detected starting from April 24, 2010, and the correlation with the most energetic seismic events has been tentatively proposed. Highlights: Since 2004 an automatic continuous monitoring station is operating at Mt. Amiata. Small variations of selected geochemical parameters are accurately detected. CO2 anomalous variations appear to be correlated with regional seismic activity. CO2 time series were processed with multiple statistical methods.