Original Research ARTICLE
Automatic Filtering of Soil CO2 Flux Data. Different Statistical Approaches Applied to Long Time Series
- 1Instituto de Investigação em Vulcanologia e Avaliação de Riscos (IVAR), Portugal
- 2Centro de Informação e Vigilância Sismovulcânica dos Açores (CIVISA), Portugal
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.
A permanent soil CO2 flux station is installed at Caldeiras da Ribeira Grande area since June 2010. This degassing site is located in the north flank of Fogo Volcano, a polygenetic volcano at S. Miguel Island (Azores archipelago, Portugal). The station performs measurements based on the accumulation chamber method and has coupled several meteorological sensors. Average soil CO2 flux and soil temperature values around 1165 g m-2 d-1 and 33ºC, respectively, were measured in this site between June 2010 and June 2017. This study discusses different statistical approaches applied to the long time series recorded in this degassing area, focusing on the application of stepwise multivariate regression analysis, wavelets and Fast Fourier Transform to understand the CO2 flux variations and to detect eventual anomalous periods that can represent deep changes in the volcano feeding reservoirs.
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 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.
Keywords: Soil CO2 flux time series, Stepwise multivariate regression analysis, Fast Fourier Transform, wavelets, Seismo-volcanic monitoring
Received: 30 Jul 2018;
Accepted: 31 Oct 2018.
Edited by:Valerio Acocella, Università degli Studi Roma Tre, Italy
Reviewed by:Micol Todesco, National Institute of Geophysics and Volcanology (INGV), Italy
Agnes Mazot, GNS Science, New Zealand
Copyright: © 2018 Oliveira, Viveiros, Silva and Pacheco. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Prof. Fátima Viveiros, Instituto de Investigação em Vulcanologia e Avaliação de Riscos (IVAR), Ponta Delgada, 9500-321, Portugal, firstname.lastname@example.org