Anomalies in Radon (222Rn) concentrations prior to earthquakes have been widely documented in seismogenic areas worldwide, but questions about their predictability remain largely unanswered. Even if it is not universally accepted, the analysis of the high-resolution time series of Rn (222Rn) concentrations in groundwater, air and soil has been proposed as a suitable method to identify seismic precursors. This study, which is aimed at identifying potential gas-geochemical precursors to nearby earthquakes, analyses groundwater Rn concentrations, which were continuously measured between April 2017 and December 2019. We conducted a detailed time series analysis of dissolved Rn in two springs emerging along two active fault zones in the inner sector of the central-southern Apennines (i.e. the Matese and Morrone fault zones) in Italy. We used a simple statistical method to identify seismic precursor anomalies in Rn concentrations. Anomalies are commonly assumed as values exceeding±2σ. Furthermore, we calculated the strain radius (for which a gas-geochemical precursor was expected) and the epicentral distance (from both our monitoring stations) of each seismic event of Mw ≥3.5 that occurred in the monitoring area. Results from our ongoing research are promising and show significant correlations between seismic signals and Rn concentrations. However, longer time series data that include more energetic earthquakes are needed to shed light on the behaviour of this gas in relation to crustal deformation processes.

Optimization of dissolved Radon monitoring in groundwater to contribute to the evaluation of the seismic activity. An experience in central-southern Italy / Barberio, MARINO DOMENICO; Gori, Francesca; Barbieri, Maurizio; Billi, Andrea; Casalati, Flaminia; Franchini, Stefania; Lorenzetti, Lucrezia; Petitta, Marco. - In: SN APPLIED SCIENCES. - ISSN 2523-3963. - 2:8(2020). [10.1007/s42452-020-3185-2]

Optimization of dissolved Radon monitoring in groundwater to contribute to the evaluation of the seismic activity. An experience in central-southern Italy

Marino Domenico Barberio
;
Francesca Gori;Maurizio Barbieri;Stefania Franchini;Marco Petitta
2020

Abstract

Anomalies in Radon (222Rn) concentrations prior to earthquakes have been widely documented in seismogenic areas worldwide, but questions about their predictability remain largely unanswered. Even if it is not universally accepted, the analysis of the high-resolution time series of Rn (222Rn) concentrations in groundwater, air and soil has been proposed as a suitable method to identify seismic precursors. This study, which is aimed at identifying potential gas-geochemical precursors to nearby earthquakes, analyses groundwater Rn concentrations, which were continuously measured between April 2017 and December 2019. We conducted a detailed time series analysis of dissolved Rn in two springs emerging along two active fault zones in the inner sector of the central-southern Apennines (i.e. the Matese and Morrone fault zones) in Italy. We used a simple statistical method to identify seismic precursor anomalies in Rn concentrations. Anomalies are commonly assumed as values exceeding±2σ. Furthermore, we calculated the strain radius (for which a gas-geochemical precursor was expected) and the epicentral distance (from both our monitoring stations) of each seismic event of Mw ≥3.5 that occurred in the monitoring area. Results from our ongoing research are promising and show significant correlations between seismic signals and Rn concentrations. However, longer time series data that include more energetic earthquakes are needed to shed light on the behaviour of this gas in relation to crustal deformation processes.
2020
Radon; earthquakes; precursors; geochemistry; groundwater; springs
01 Pubblicazione su rivista::01a Articolo in rivista
Optimization of dissolved Radon monitoring in groundwater to contribute to the evaluation of the seismic activity. An experience in central-southern Italy / Barberio, MARINO DOMENICO; Gori, Francesca; Barbieri, Maurizio; Billi, Andrea; Casalati, Flaminia; Franchini, Stefania; Lorenzetti, Lucrezia; Petitta, Marco. - In: SN APPLIED SCIENCES. - ISSN 2523-3963. - 2:8(2020). [10.1007/s42452-020-3185-2]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1476246
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