Automatic Filtering of Soil CO2 Flux Data; Different Statistical Approaches Applied to Long Time Series

• Main Authors: Sérgio Oliveira, Fátima Viveiros, Catarina Silva, Joana E. Pacheco
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2018-11-01
• Series: Frontiers in Earth Science
• Subjects: soil CO2 flux time series; stepwise multivariate regression analysis; Fast Fourier Transform; wavelets; seismo-volcanic monitoring
• Online Access: https://www.frontiersin.org/article/10.3389/feart.2018.00208/full

Monitoring soil CO2 diffuse degassing areas has become more relevant in the last decades to understand seismic and/or volcanic activity. These studies are specially valuable for volcanic areas without visible manifestations of volcanism, such as fumaroles or thermal springs. The development and installation of permanent soil CO2 flux instruments has allowed to acquire long time series in different volcanic environments, and the results obtained highlight the influence of environmental variables on the gas flux variations. Filtering the influence of these external variables on the gas flux is crucial to understand deep processes on the volcanic system. This study focuses on the discussion of different statistical approaches applied to the long time series recorded in a diffuse degassing area of the Azores archipelago, mainly on the application of stepwise multivariate regression analysis, wavelets and Fast Fourier transforms to understand the CO2 flux variations and to detect eventual anomalous periods that can represent deep changes in the volcano feeding reservoirs. A permanent soil CO2 flux station is installed at Caldeiras da Ribeira Grande area since June 2010. This degassing site is located at Fogo Volcano, a polygenetic volcano at S. Miguel Island. The station performs measurements based on the accumulation chamber method and has coupled several environmental sensors. Average soil CO2 flux and soil temperature values around 1,165 gm−2 d−1 and 33°C, respectively, were measured in this site between June 2010 and June 2017. Multivariate regression analysis shows that about 47% of the soil CO2 flux variations are explained by the effect of the soil and air temperature, wind speed, and soil water content. Spectral analysis highlights the existence of 24 h cycles in the soil CO2 flux time series, mainly during the summer period. The filtered time series showed some anomalous periods and a correlation with the geophysical data recorded on the area was carried out. The models proposed have been applied on a near real-time automatic monitoring system and implementation of these approaches will be profitable in any volcano observatory of the world since it allows a fast understanding of the degassing processes and contribute to recognize unrest periods.