GNGTS 2023 - Atti del 41° Convegno Nazionale

Session 1.3 GNGTS 2023 Analysis of SAR Data Unveils Ongoing Deep Magma Inflation at Campi Flegrei Caldera (Italy) A. Amoruso, L. Crescentini Department of Physics, University of Salerno, Fisciano, Italy We investigate the 1993-2000 subsidence and part (2015-2022) of the ongoing uplift at Campi Flegrei caldera, using ground displacements from ERS-ENVISAT and Sentinel-1A SAR images respectively. Although more recent line-of-sight (LOS) displacement time series from ERS-ENVISAT imagery are available (Polcari et al., 2022), here we rely on a subset of the displacement time series in Amoruso et al. (2014), which were obtained by CNR-IREA through the Small BAseline Subset Differential Synthetic Aperture Radar Interferometry (SBAS DInSAR) technique (Berardino et al., 2002), since they are in very good agreement with levelling data during 1993-2000. The full ERS-ENVISAT time series include 138 ascending--orbit images from 1993.03 to 2010.73 and 155 descending--orbit images from 1992.44 to 2010.71. As for the Sentinel-1A time series, we use Level-1 Single Look Complex (SLC) interferometric wide swath imagery, provided by the European Space Agency (ESA) and downloaded from the Alaska Satellite Facility. SLC products consist of focused SAR data, that are geo-referenced using orbit and attitude data from the satellite and provided in slant-range geometry. We created LOS displacement time series from ascending-orbit and descending-orbit images using SBAS within the GMTSAR InSAR processing system (Sandwell et al., 2011), through the Sentinel TOPS-mode processing and interferometry procedure. The time series include 193 ascending-orbit images from 2014.84 to 2022.22 and 192 descending-orbit images from 2015.26 to 2022.19. For each of the two time periods (1993-2000 and 2015-2022) we combine LOS displacements to obtain vertical and eastward displacements, and apply the Empirical Orthogonal Function analysis (e. g. Navarra and Simoncini, 2010) to these latter time series (treated as a single data set) to decompose space-time fields into separated modes, consisting of uncorrelated spatial patterns and associated temporal evolutions. We only retain the first mode, since it captures the main deformation during both investigated periods, is the sole mode related to long-lasting (years) processes, and is less affected by noise than original data.