This paper investigated the achievable accuracy from a low-cost RTK (Real Time Kinematic)/PPK (Post Processing Kinematic) GNSS (Global Navigation Satellite Systems) system installed on board a UAV (Unmanned Aerial Vehicle), employing three different types of GNSS Bases (Alloy, RS2 and RING) working in PPK mode. To evaluate the quality of the results, a set of seven GCPs (Ground Control Points) measured by means of the NRTK (Network Real Time Kinematic) technique was used. The outcomes show a RMSE (Root Mean Square Error) of 0.0189 m for an ALLOY Base, 0.0194 m for an RS2 Base and 0.0511 m for RING Base, respectively, on the vertical value of DEMs (Digital Elevation Models) obtained by a photogrammetric process. This indicates that, when changing the Base for the PPK, the solutions are different, but they can still be considered adequate for precision positioning with UAVs, especially when GCPs could be used with some difficulty. Therefore, the integration of a RTK/PPK GNSS module on a UAV allows the reconstruction of a highly detailed and precise DEM without using GCPs and provides the possibility to carry out surveys in inaccessible areas.

The Istituto Nazionale di Geofisica e Vulcanologia (INGV) is an Italian research institution, with focus on Earth Sciences. INGV runs the Italian National Seismic Network (Rete Sismica Nazionale, RSN) and other networks at national scale for monitoring earthquakes and tsunami as a part of the National Civil Protection System coordinated by the Italian Department of Civil Protection (Dipartimento di Protezione Civile, DPC). RSN is composed of about 400 stations, mainly broadband, installed in the Country and in the surrounding regions; about 110 stations feature also co-located strong motion instruments, and about 180 have GPS receivers and belong to the National GPS network (Rete Integrata Nazionale GPS, RING). The data acquisition system was designed to accomplish, in near-real-time, automatic earthquake detection, hypocenter and magnitude determination, moment tensors, shake maps and other products of interest for DPC. Database archiving of all parametric results are closely linked to the existing procedures of the INGV seismic monitoring environment and surveillance procedures. INGV is one of the primary nodes of ORFEUS (Observatories & Research Facilities for European Seismology) EIDA (European Integrated Data Archive) for the archiving and distribution of continuous, quality checked seismic data. The strong motion network data are archived and distributed both in EIDA and in event based archives; GPS data, from the RING network are also archived, analyzed and distributed at INGV. Overall, the Italian earthquake surveillance service provides, in quasi real-time, hypocenter parameters to the DPC. These are then revised routinely by the analysts of the Italian Seismic Bulletin (Bollettino Sismico Italiano, BSI). The results are published on the web, these are available to both the scientific community and the general public. The INGV surveillance includes a pre-operational tsunami alert service since INGV is one of the Tsunami Service providers of the North-eastern Atlantic and Mediterranean Tsunami warning System (NEAMTWS).

In this work, Multi Temporal Interferometry techniques (MTI) based on advanced synthetic aperture radar differential interferometry (A-DInSAR) have been investigated for the monitoring of deformation phenomena in slow kinematics. A-DInSAR methodologies include both Coherence-based type, as well as Small Baseline Subset (SBAS) that Permanent Scatterers (PS). These techniques are capable to provide wide-area coverage and precise, spatially dense information on ground surface deformations. MTI techniques have been applied to the town of Stigliano (MT) in Basilicata Region (Southern Italy), where the social center building has been destroyed after the reactivation of a known landslide. A direct comparison of the results has been shown that PS and SBAS techniques are comparable in terms of obtained coherent areas and displacement patterns, with slightly different velocity values for individual points. In particular, PS furnished a range of velocity between −5/−25 mm/year, while for SBAS we found values around −5/−15 mm/year. Furthermore, on the crown of the landslide body, a Robotics Total Station measuring distance values on 24 points has been installed. The displacement values obtained are in agreement with the results of the MTI analysis, showing as these techniques could be a useful in the case of early-warning situations.

The great potential of the Global Navigation Satellite System (GNSS) in monitoring ground deformation is widely recognized. As with other geophysical data, GNSS time series can be significantly noisy, hiding elusive ground deformation signals. Several denoising techniques have been proposed to improve the signal-to-noise ratio over the years. One of the most effective denoising techniques has been proved to be multi-resolution decomposition through the discrete wavelet transform. However, wavelet analysis requires long data sets to be effective, as well as long computation times, that hinder its use as a real or near real-time monitoring tool. We propose training by a Convolutional Neural Network (CNN) to perform the equivalent of wavelet analysis to overcome these limitations. Once trained, the CNN model provides answers within seconds, making it feasible as a real-time data analysis tool. Our Machine Learning algorithm is tested on daily GNSS time series collected in the Campi Flegrei caldera (Southern Italy), which is a highly volcanic risk area. Without significant gaps, the retrieved RMSE and R² values vary in the ranges 0.65–0.98 and 0.06–0.52 cm, respectively. These results are encouraging, as they hint at the possibility of applying this methodology in more effective real-time monitoring solutions for active volcanoes.

In May-July 2012, Emilia Romagna (northern Italy) was struck by a significant seismic sequence, which was characterized by two moderate-magnitude earthquakes: a Ml 5.9 event on May 20, 2012, at 02:03:53 UTC, and a Ml 5.8 event on May 29, 2012, at 07:00:03 UTC, about 12 km to the west of the first mainshock. The earthquake sequence produced a total of 20 casualties and severe and widespread damage, mainly to historical and commercial buildings. A detailed description of the seismic sequence can be found in Sco-gnamiglio et al. [2012, this volume]. The largest of the earthquake static displacements were recorded by tens of continuous global positioning system (cGPS) stations, as described in Serpelloni et al. [2012, this volume]. Most of these stations were operating with a sampling frequency of 1 Hz, and they belonged to scientific or commercial networks: RING (http://ring.gm.ingv.it); ITALPOS (http://smartnet.leica-geosystems.it); GeoTop (http://www.netgeo.it); Fondazione Geometri Emilia Romagna (http://www.gpsemiliaromagna.it; Lombardia [http://www.gpslombardia.it); and Veneto (http://147.162.229.63). Some hours after the first mainshock, the sampling frequency of the near-field RING stations (SBPO and MODE) were switched to 20 Hz, thus recording the coseismic displacements produced by the May 29, 2012, earthquake at higher frequency. This sampling frequency was previously used for the detection of coseismic dynamic displacements only for the Mw 9 Tohoku-Oki 2011 event [Colosimo et al. 2011b]. Thus, the 20-Hz-sampling displacements for the Tohoku-Oki 2011 earthquake and the May 29, 2012, Emilia event might represent important recordings to investigate coseismic contributions at frequencies higher than 1 Hz with GPS. In the present study, after the description of the high-rate GPS (HRGPS) data analysis, we will show and compare the preliminary results. Then, for the two mainshocks, we will compare the displacements recorded by the HRGPS (1 Hz up to 20 Hz) and the strong-motion time histories (100 Hz) at MODE, where the different instruments were approximately co-located (Figure 1, inset, relative distance of ca. 90 m). […]

The M_L 5.8 earthquake that hit the island of Crete on 27 September 2021 is analysed with InSAR (Interferometry from Synthetic Aperture Radar) and GNSS (Global Navigation Satellite System) data. The purpose of this work is to create a model with sufficient detail for the geophysical processes that take place in several kilometres below the earth’s surface and improve our ability to observe active tectonic processes using geodetic and seismic data. InSAR coseismic displacements maps show negative values along the LOS of ~18 cm for the ascending orbit and ~20 cm for the descending one. Similarly, the GNSS data of three permanent stations were used in PPK (Post Processing Kinematic) mode to (i) estimate the coseismic shifts, highlighting the same range of values as the InSAR, (ii) model the deformation of the ground associated with the main shock, and (iii) validate InSAR results by combining GNSS and InSAR data. This allowed us to constrain the geometric characteristics of the seismogenic fault and the slip distribution on it. Our model, which stands on a joint inversion of the InSAR and GNSS data, highlights a major rupture surface striking 214°, dipping 50° NW and extending at depth from 2.5 km down to 12 km. The kinematics is almost dip-slip normal (rake −106°), while a maximum slip of ~1.0 m occurred at a depth of ca. 6 km. The crucial though indirect role of inherited tectonic structures affecting the seismogenic crustal volume is also discussed suggesting their influence on the surrounding stress field and their capacity to dynamically merge distinct fault segments.