Sinkholes linked to cover evaporite karst in urban environments still represent a challenge in terms of their clear identification and mapping considering the rehash and man-made structures. In the present research, we have proposed and tested a methodology to identify the subsiding features through an integrated and non-invasive multi-scale approach combining seismic reflection, PS-InSAR (PSI), leveling and full 3D Ground Penetrating Radar (GPR), and thus overpassing the limits of each method. The analysis was conducted in a small village in the Alta Val Tagliamento Valley (Friuli Venezia Giulia region, NE Italy). Here, sinkholes have been reported for a long time as well as the hazards linked to their presence. Within past years, several houses have been demolished and at present many of them are damaged. The PSI investigation allowed the identification of an area with higher vertical velocities; seismic reflection imagined the covered karst bedrock, identifying three depocenters; leveling data presented a downward displacement comparable with PSI results; 3D GPR, applied here for the first time in the study and characterization of sinkholes, defined shallow sinking features. Combining all the obtained results with accurate field observations, we identified and mapped the highest vulnerable zone.

Geodetic data can detect and estimate deformation signals and rates due to natural and anthropogenic phenomena. In the present study, we focus on northeastern Italy, an area characterized by ~1.5–3 mm/yr of convergence rates due to the collision of Adria-Eurasia plates and active subsidence along the coasts. To define the rates and trends of tectonic and subsidence signals, we use a Multi-Temporal InSAR (MT-InSAR) approach called the Stanford Method for Persistent Scatterers (StaMPS), which is based on the detection of coherent and temporally stable pixels in a stack of single-master differential interferograms. We use Sentinel-1 SAR images along ascending and descending orbits spanning the 2015–2019 temporal interval as inputs for Persistent Scatterers InSAR (PSI) processing. We apply spatial-temporal filters and post-processing steps to reduce unrealistic results. Finally, we calibrate InSAR measurements using GNSS velocities derived from permanent stations available in the study area. Our results consist of mean ground velocity maps showing the displacement rates along the radar Line-Of-Sight for each satellite track, from which we estimate the east–west and vertical velocity components. Our results provide a detailed and original view of active vertical and horizontal displacement rates over the whole region, allowing the detection of spatial velocity gradients, which are particularly relevant to a better understanding of the seismogenic potential of the area. As regards the subsidence along the coasts, our measurements confirm the correlation between subsidence and the geological setting of the study area, with rates of ~2–4 mm/yr between the Venezia and Marano lagoons, and lower than 1 mm/yr near Grado.

Sinkholes linked to cover evaporite karst in urban environments still represent a challenge in terms of clear identification and mapping considering the anthropic rehash and the presence of man-made structures.We propose and tested a methodology to identify the subsiding features in an urban area within a cover evaporite karst environment, through an integrated and non-invasive multi-scale approach combining seismic reflection, DInSAR, leveling and full 3D GPR.The analysis was conducted in a small village in the Tagliamento valley (Friuli Venezia Giulia region, NE Italy) named Quinis, where sinkholes are reported since a long time as well as the hazard linked to their presence: within the years, several houses have been demolished and at present many of them are damaged.First we applied each methodology independently and after we compared, combined and integrated them to obtain more coherent and cross-validates results. Seismic reflection imagined the covered karst bedrock identifying three depocenters; DInSAR investigation allowed to identify an area with higher vertical velocities; leveling data presented a downward displacement comparable with DInSAR results; 3D GPR, applied here for the first time in the study and characterization of sinkholes, clearly defined shallow sinking features imaging also under a shallow dense pipe network. Combining all the obtained results with accurate field observations we identified and map the highest vulnerable zones.The final result is the combining of the geophysical, DInSAR and leveling information, while also locating the damaged buildings, the local asphalt pavement breaks or renovation and the buildings which are nowadays demolished, by using vintage photographs and historical maps. The data are consistent, being the most relevant present damages and the demolished building within the zones with higher sinking velocity on the base of both leveling and DInSAR. Geophysically imaged depocenters lie within the most critical area and perfectly correlate with the local pavement damages.In a complex geological and hydrological framework, as in the study area, a multidisciplinary and multi-scale approach is mandatory to identify and map the zone most affected by sinking phenomena. While punctual data such as borehole stratigraphy, local groundwater level variations with time, extensometers measurements and geotechnical parameters are useful to highlight local hazard due to occurring deformation, the proposed integrated methodology addresses a complete and quantitative assessment of the vulnerability of the area. It’s fundamental, especially in anthropized environments, using different integrated techniques, without forgetting the role of the fieldwork of the geologists who can detect the precursors or already occurred, even elusive, signs of the ongoing or incipient sinking.

Ground displacement measurements are fundamental for investigating the surface effects of numerous natural and anthropogenic processes acting within the same region. Spatial geodesy measures the displacement of the ground due to the sum of multi-scale processes, i.e. processes that occur at different spatial and temporal scales. The joint action of these phenomena can generate surface deformations characterized by constant trends or transients over time or even by cyclical variations that generate seasonal signals in the displacement time series, with an annual or multi-annual period. Separating the contribution of each phenomenon in the displacement measurements is a complicated objective to achieve because it is necessary to identify within the GNSS and InSAR time series the signals associated with the various processes and to have a large amount of information relating to the geological, geophysical and hydrological characteristics.The target area of this work (coastal area of the Po Plain, Italy) is affected by various processes of natural and anthropogenic origin, such as the subsoil water pumping, the compaction of sediments throughout the plain area, the hydrocarbon cultivation at the numerous onshore and offshore active concessions, and also the active tectonic process linked to the convergence between the Northern Apennines and the Adriatic plate.Aim of this work is to develop a systematic method of analysis both at regional and local scales of the GNSS and InSAR displacement time series using signal decomposition techniques to identify the main ongoing deformation processes. Extracted signals are compared with the time series of all available physical, hydrological, geophysical and geological parameters to identify the main deformation sources causing the observed displacements. In particular, considering the differences in lengths and temporal samplings among the datasets, all the measurements have been standardized in the same formats through an open-source code, allowing for the comparison among the different types of data to investigate any associations and correlations, and executing also a data quality analysis. Furthermore, a Matlab-based code has been developed to quickly and automatically analyze the InSAR displacement time series. The code provides information on linear, non-linear, cyclic and/or seasonal components, by using frequency analysis (spectral analysis via Lomb-Scargle periodogram to evaluate most significant components and their periodicity), and by means of the estimate the Non-Linearity Index (INL), defined as the ratio between the long-term signal variability and the high-frequency noise variability. Such a code is general and could be applied to several areas of interest.

Geodetic data provide useful information on surface deformation over long period of time. Applying time series methods to geodetic data, several phenomena were studied. In particular, the potentials of geodetic data were exploited to detect and measure slow tectonic signals such as interseismic strain accumulation. During the interseismic period, when the faults are locked, an accumulation of deformation can occur in response to active tectonic stresses. Considering that such energy can be released through earthquakes, the estimation of surface deformation and the long-term strain rate reveals itself a useful approach for seismic hazard investigations. In this study, we used remote sensing Synthetic Aperture Radar data to evaluate the ground deformation in the Southeastern Alps (Northeastern Italy), an area characterized by an active convergent regime (Adria plate motion is ~ 2mm/yr) as well as several active tectonic structures. We used SAR images provided by Sentinel-1A/B satellites spanning the 2015-2019 temporal interval by applying the multi temporal Small Baseline Subset Interferometry (SBAS) technique. The method is based on a combination of a large number of interferograms characterized by small temporal and geometric baseline in order to reduce decorrelation effects and increase the spatial coverage over the area of interest. The outcomes consist of displacement time series and a mean ground velocity map for each coherent pixels with respect to the satellite Line-of-Sight (LoS). Some detected patterns can be attributed to subsidence phenomena, affecting the plain in the area under analysis, and due to the compaction of the sediments.

Geodetic data play a crucial role in the detection of surface deformation related to active tectonic processes. The present study aimed to investigate the Northeastern Italian sector, characterized by a convergent regime due to the NNW-ward motion of the Adria microplate towards the Eurasian plate, at a rate of ~ 2mm/yr. N-S shortening is accommodated by fold and thrust systems in the Alpine chain and buried below the Friuli-Venetian plain sediments. We used InSAR and GNSS data respectively in 2015-2019 and 2000-2020 time interval to estimate the surface kinematics and deformation pattern of the area. We processed the SAR images acquired by the European satellites Sentinel 1A/B from ascending and descending tracks by using the Stanford Method for Persistent Scatterers (StaMPS). A post-processing of the resulting Line-Of-Sight (LOS) deformation time series was carried out by applying a spatial-temporal filter and calibrating using the velocities provided by GNSS stations. Finally, the post-processed ascending and descending LOS measurements were combined to solve for the vertical and horizontal (east-west) deformation components. We observed a positive vertical signal toward the Alps, in the northern region of Veneto and Friuli-Venezia Giulia. Moreover, we observed a significant negative vertical signal located in the plain and in the coastal zones due to the subsidence that strongly affects these areas. Horizontal velocities with rate of 1-2 mm/yr are observed close to main tectonic structures, especially in the eastern and the northwestern sector of the study area, where GNSS data reveal higher shortening rate.