## 3 Materials and Methods
### P-SBAS InSAR Processing  
The multi-interferogram processing was executed in ESA's GEP by exploiting the \"CNR-IREA P-SBAS Sentinel-1 processing on-demand\" service v.1.0.0 within the \"CNR-IREA SBAS Ground Motion Services\" Thematic App (Figure 3). The processing approach builds upon the SBAS technique [6], and has been adapted to efficiently run on high-performance distributed-computing infrastructure (e.g., grid and cloud computing) [13], and tailored for Sentinel-1 IW TOPS data processing [54]. Figure 3: Selection of the available Sentinel-1 Interferometric Wide (IW) swath data over the area of interest (AOI) and setup of the Parallel Small BAseline Subset (P-SBAS) processing job in the Geohazards Exploitation Platform (GEP) geobrowser. Since its development in early 2010s, a huge number of experiments and test-cases have been run with P-SBAS by the developers team at CNR-IREA (e.g., [13; 19; 54; 55; 56; 57; 58; 59]), as well as by independent research teams in the GEP Early Adopters Programme (e.g., [60; 61; 62; 63; 64; 65; 66; 67; 68; 69]), using ERS-1/2, ENVISAT, COSMO-SkyMed, and Sentinel-1 SAR imagery. Not only local and city scale, but also regional (e.g., [13; 57; 58; 65]), national (i.e., Italy; [54; 59]), and up to continental (i.e., whole Europe; [22]) scale studies have been successfully carried out. Moreover, different computing infrastructure setups have been trialed for the data processing, including CNR-IREA's own cluster (e.g., [19; 56; 57; 70]), the Amazon Web Services (AWS) cloud-computing environment (e.g., [56; 57; 58; 13; 54; 56; 59; 13; 55; 57; 14; 58]), ESA's Grid-Processing On Demand (G-POD) (e.g., [55; 60; 65; 68; 69; 71; 72]) and GEP (e.g., [63; 64; 67; 24]), and more recently the ONDA Data and Information Access Service (DIAS) [22]. In this framework, the published literature shows a yet relatively limited number of applications that aimed to exploit this service for geological investigations (e.g., [61; 63; 23; 64; 24]), since the release of the P-SBAS Sentinel-1 on-demand service within the GEP in early 2019. Therefore, this study also contributes to this research direction. The processing in GEP began with the retrieval of the input SLC data from the image archives, which is mainly based on the repositories of the ESA Open Access Hub, ONDA DIAS, and CREODIAS. Each SLC data stack was co-registered at the single-burst level ensuring very high coregistration accuracy (in the order of 1/1000 of the azimuth pixel size), as required for TOPS data due to the large along-track Doppler centroid variations [73]. Small temporal and perpendicular baseline pairs were selected to form the interferograms, with the aim to reduce interferometric phase decorrelation [74] by limiting the maximum number of consecutive acquisitions to be used to form the pairs [54], and by exploiting the narrow orbital tube (i.e., 100 m diameter during nominal mode operations; [50]). A complex multi-looking operation in azimuth and range (5 by 20) was applied to mitigate the decorrelation noise in the interferograms and generate the coherence maps, thus reaching ground pixel spacing of \(\sim\)90 m, as per the default parameter setting of the current P-SBAS service version in the GEP. Topographic phase components were derived and subtracted based on NASA's 1 arcsec (i.e., 30 m) resolution Shuttle Radar Topography Mission (SRTM) Digital Surface Model (DSM). Multi-looked burst interferograms were mosaicked to sub-swaths, and then the two sub-swaths covering areas of land were mosaicked together to reach two-thirds of the swath. i.e., \(\sim\)170 km (see stack coverage in Figure 3). The interferograms were then noise-filtered using the Goldstein filter with 0.5 weight [75], and optimized triangular networks of interferograms were identified [76], including 861 interferograms for the ascending stack, and 828 for the descending stack. Temporal and spatial phase-unwrapping was implemented with the extended minimum cost flow (EMCF) algorithm [77].