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Abstract Since early 2021, peculiar Volcano-Tectonic (VT) seismic sequences with very short inter-event times have become increasingly frequent and evident in the Campi Flegrei caldera (Italy), which has been experiencing a long-term unrest since 2005. During the same period the ground deformation (uplift), seismicity and gas emission that characterize the current unrest showed an acceleration. Within this type of seismic sequence, we identify burst-like swarms, characterized by inter-event times so short that they are often not easily recognizable. Here we show that these sequences are located in an area encompassing the main hydrothermal field and a zone affected by a geodetic anomaly (a region that uplifts less rapidly than the surroundings), which became evident in 2021. This type of seismicity has been associated with phreatic explosions and critical phases of unrest in other volcanoes and currently characterizes the state of activity of Campi Flegrei caldera.

This article presents an innovative, parallel implementation of the Small BAseline Subset (SBAS) approach to automatically and efficiently process large volumes of multitemporal differential synthetic aperture radar (SAR) interferometry (DInSAR) interferograms generated at the native full spatial resolution of the SAR images. The starting point of the developed full-resolution parallel-SBAS (FR P-SBAS) technique involves some algorithmic extensions for improving the quality of the DInSAR time series to effectively analyze extended deformations and localized displacement phenomena, such as those affecting single buildings and infrastructures. The main focus of the work is on the efficient and scalable FR P-SBAS processing chain implementation, extensively exploiting graphical processing unit (GPU) architectures. Moreover, the presented scalability analysis demonstrates the GPU capability of efficiently generating full-resolution displacement time series starting from large DInSAR datasets. Furthermore, it is also shown that the implemented processing solution easily allows us to deal with SAR data acquired through the Stripmap and TOPS modes. To assess the quality of the generated DInSAR products, an extensive experimental analysis is also shown, based on long sequences of X-Band COSMO-SkyMed Stripmap and C-Band Sentinel-1 TOPS acquisitions relevant to the Campi Flegrei Caldera (Southern Italy), which is monitored through a dense GNSS network. The presented results demonstrate the effectiveness of the FR P-SBAS processing chain in retrieving multifrequency and multiplatform displacement time series at the full spatial resolution with subcentimetric accuracy and in very short time frames, from a few hours for the COSMO-SkyMed datasets up to some tens of hours for the Sentinel-1 case.

We present in this work the new advances of the differential synthetic aperture radar (SAR) interferometry (DInSAR) technique referred to as the parallel Small BAseline Subset (P-SBAS) approach for the generation of displacement time series from the recently available L-band SAR image sequences acquired by the Argentinian SAOCOM-1 constellation. In particular, we first discuss the algorithmic extensions allowing us to retrieve SAOCOM-1 DInSAR products, with a main focus on the exploitation of Stripmap SAR images and multilook interferograms. These algorithmic improvements are relevant to the adjacent single look complex (SLC) image slices merging to the multitemporal residual orbital artifacts mitigation and to the phase unwrapping procedure enhancement. Subsequently, the effectiveness of the extended P-SBAS processing chain is shown through a comparative analysis between the achieved SAOCOM-1 DInSAR results and the corresponding ones relevant to Sentinel-1 and COSMO-SkyMed acquisitions. Moreover, a comparison between the SAOCOM-1 displacement time series and the GNSS measurements, relevant to the Campi Flegrei Caldera (Italy), is presented. This shows that the mean standard deviation value of the differences between the DInSAR and the GNSS time series, the latter projected in the radar line of sight, is about 1 cm, further confirming the effectiveness of the P-SBAS processing chain extension.

Campi Flegrei caldera is an Italian high-risk volcano experiencing a progressively more intense long-term uplift, accompanied by increasing seismicity and geochemical emissions over the last two decades. Ground deformation shows an axisymmetric bell-shaped pattern, with a maximum uplift of about 120 cm, from 2005, in the caldera central area. We analyzed Sentinel-1 and COSMO-SkyMed Multi-Temporal DInSAR measurements and GNSS data to reveal and investigate a geodetic anomaly that has clearly manifested since 2021, locally deviating from the typical bell-shaped deformation pattern. This anomaly is located east of Pozzuoli town, in the Mt. Olibano–Accademia area, covers an area of about 1.3 km2 and shows, in comparison to surrounding areas, a maximum uplift deficit of about 9 cm between 2021 and 2023. To investigate the anomaly causes, we analyzed the caldera seismicity and inverted the DInSAR data to determine the primary source of the ground deformation pattern, which is consistent with a penny-shaped source located approximately 3800 m beneath the Pozzuoli town, with a radius of about 1200 m. We also found that the time evolution of the uplift deficit in the geodetic anomaly area correlates well with the earthquake occurrence, with the greater magnitude events clustering in this area. These considerations suggest the geodetic anomaly is a local response to the tensile stress regime produced by the inflating primary deformation source. This phenomenon can be influenced by the Mt. Olibano–Accademia lava domes lithological heterogeneities that may induce a localized reaction to ground deformation during the inflationary phase. Our interpretation aligns with the concentration of earthquakes and hydrothermal fluid emissions in this area, indicating the presence of faults, fractures, and fluid circulation. Accordingly, the geodetic anomaly area represents a zone of crustal weakness that requires careful monitoring and study.

Wide-area ground motion monitoring is nowadays achievable via advanced Differential Interferometry SAR (A-DInSAR) techniques which benefit from the availability of large sets of Copernicus Sentinel-1 images. However, it is of primary importance to implement automated solutions aimed at performing integrated analysis of large amounts of interferometric data. To effectively detect high-displacement areas and classify ground motion sources, here we explore the feasibility of a machine learning-based approach. This is achieved by applying the random forest (RF) technique to large-scale deformation maps spanning 2015–2018. Focusing on the northern part of Italy, we train the model to identify landslide, subsidence, and mining-related ground motion with which to construct a balanced training dataset. The presence of noisy signals and other sources of deformation is also tackled within the model construction. The proposed approach relies on the use of explanatory variables extracted from the A-DInSAR datasets and from freely accessible informative layers such as Digital Elevation Model (DEM), land cover maps, and geohazard inventories. In general, the model performance is very promising as we achieved an overall accuracy of 0.97, a true positive rate of 0.94 and an F1-Score of 0.93. The obtained outcomes demonstrate that such transferable and automated approach may constitute an asset for stakeholders in the framework of geohazards risk management.

In this work we describe the implementation of a processing chain for a fully automatic modeling of the seismic source parameters and its slip distribution through the inversion of the InSAR displacements generated from the EPOSAR service. This processing chain consists of a suite of procedures and algorithms handling a sequence of steps: selection of the highest quality InSAR datasets, definition of the area of interest, image sampling, non-linear and linear inversions to get, respectively, the source geometry and its slip distribution. A set of side procedures and interfaces also allows an interactive refinement and the publication of results, consisting of scientific data and graphical outputs. The whole procedure has been developed, tested and validated by considering 100 events with magnitudes between 5.5 and 8.2, worldwide distributed and covering an exhaustive range of mechanisms and tectonic contexts.Main aim of this work is describing the implementation of the automatic modeling procedures, used to produce solutions in real time, already during the emergency phase. These sources, validated by experts before their publication, can be a reference for operational purposes and initial scientific analyses. The creation of this repository sets also the framework to store, out of the emergency time, more sophisticated solutions, manually revised and/or with peer-review quality.

The 2019 Le Teil earthquake in Southern France may have been triggered by the stress change from 180 years of mass removal at a nearby cement quarry, according to satellite-based observations and seismological analyses of the rupture parameters

Abstract A major goal in volcanology is to be able to constrain the physical properties of a volcanic system using surface observations. The behaviour of a volcanic system following an eruption can provide powerful constraints on these properties and can provide valuable information for understanding future hazard. We use spatially and temporally dense observations of surface deformation following the 12 June 2011 eruption of Nabro (Eritrea) to place constraints on the mechanics of its subsurface volcanic system. Nabro was imaged 129 times by TerraSAR-X and COSMO-SkyMed satellites during a 15-month period following the eruption. We have produced a detailed time series of the line-of-sight (LOS) displacements at Nabro, finding that the volcano subsides during the entire observation period at a decaying rate. We found significant atmospheric artefacts remained in the data set after a standard spatio-temporal filter was applied. Applying an empirical correction using a linear phase-elevation relationship removed artefacts but also removed real topographically correlated deformation. Instead, we were able to correct each SAR acquisition using independent delay estimates derived from the ECMWF ERA-Interim (ERA-I) global atmospheric model. The corrected time series can be modelled with the deflation of a Mogi source at ∼ 6.4 ± 0.3 km depth. Modelling the time series using viscoelastic relaxation of a shell which surrounds a spherical magma chamber can explain the observed subsidence without a source of further volume loss if the magma is compressible. CO2 outgassing is also a possible cause of continued subsidence. Contraction due to cooling and crystallisation, however, is probably minor. If any post-eruptive recharge of the magmatic system at Nabro is occurring, the rate of recharge must be slower than the post-eruptive relaxation processes. Combined with the lack of pre-eruptive inflation, we suggest that recharge of the magmatic system at Nabro either occurs at a rate that is slower than our detection limit, or it occurs episodically. This case study demonstrates the power of long, dense geodetic time series at volcanoes.

We present an efficient Cloud Computing (CC) implementation of the Parallel Small BAseline Subset (P-SBAS) algorithm, which is an advanced Differential Interferometric Synthetic Aperture Radar (DInSAR) technique for the generation of Earth surface displacement time series through distributed computing infrastructures. The rationale of our approach consists in properly distributing the large data volumes and the processing tasks involved in the P-SBAS chain among the available (virtual and/or physical) computing nodes of the CC infrastructure, so that each one of these elements can concurrently work on data that are physically stored on its own local volume. To do this, both an ad hoc management of the data flow and an appropriate scheduling of the parallel jobs have been also implemented to properly handle the high complexity of the P-SBAS workflow. The proposed solution allows minimizing the overall data transfer and network load, thus improving the P-SBAS efficiency and scalability within the exploited CC environments. The presented P-SBAS implementation has been extensively validated through two experimental analyses, which have been carried out by exploiting the Amazon Web Services (AWS) Elastic Cloud Compute (EC2) resources. The former analysis involves the processing of a large (128 SAR images) COSMO-SkyMed dataset, which has been performed by exploiting up to 64 computing nodes, and is aimed at demonstrating the P-SBAS scalable performances. The latter allows us to show the P-SBAS capability to generate DInSAR results at a regional scale (150 000 km2 in Southern California) in a very short time (about 9 h), by simultaneously processing 18 ENVISAT frames that correspond to a total of 741 SAR images, exploiting in parallel 144 AWS computing nodes. The presented results confirm the effectiveness of the proposed P-SBAS CC solution, which may contribute to further extend the frontiers of the DInSAR investigation at a very large scale.

We present in this comment a Multi-Temporal SAR Interferometry (MT-InSAR) analysis showing that the results published by Milillo et al. (2019) in the Remote Sensing Journal, presenting the evidence of space geodetic observations relevant to displacements occurring before the collapse of the Morandi Bridge, happened in Genova (Italy) on the 14 August 2018, are questionable. In particular, we focus on the InSAR results obtained by Milillo et al. (2019) by processing the 3 m × 3 m resolution COSMO-SkyMed (CSK) data collected from ascending and descending orbits on the area of interest. These results, thanks to the high spatial resolution and the short revisit time characterizing this multi-orbit SAR dataset, represent the cornerstone of their analysis. The main findings of their study allow Milillo et al. to conclude that the InSAR processing of this COSMO-SkyMed dataset reveals the increased deformation magnitude over time of points located near the strands of the deck next to the collapsed pier, between 12 March 2017 and August 2018. In this comment, we show the results obtained by the IREA-CNR SAR team after processing the same ascending and descending CSK dataset, but by using two alternative and independent processing techniques: the Small BAseline Subset (SBAS) and the Advanced Tomographic SAR (TomoSAR) approaches, respectively. Our analysis shows that, although both the SBAS and the TomoSAR analyses allow achieving denser coherent pixel maps relevant to the Morandi bridge, nothing of the pre-collapse large displacements reported in Milillo et al. (2019) appears in our results, leading us to deeply disagree with the findings of their InSAR analysis.

We present in this work an advanced processing pipeline for continental scale differential synthetic aperture radar (DInSAR) deformation time series generation, which is based on the parallel small baseline subset (P-SBAS) approach and on the joint exploitation of Sentinel-1 (S-1) interferometric wide swath (IWS) SAR data, continuous global navigation satellite system (GNSS) position time-series, and cloud computing (CC) resources. We first briefly describe the basic rationale of the adopted P-SBAS processing approach, tailored to deal with S-1 IWS SAR data and to be implemented in a CC environment, highlighting the innovative solutions that have been introduced in the processing chain we present. They mainly consist in a series of procedures that properly exploit the available GNSS time series with the aim of identifying and filtering out possible residual atmospheric artifacts that may affect the DInSAR measurements. Moreover, significant efforts have been carried out to improve the P-SBAS processing pipeline automation and robustness, which represent crucial issues for interferometric continental scale analysis. Then, a massive experimental analysis is presented. In this case, we exploit: (i) the whole archive of S-1 IWS SAR images acquired over a large portion of Europe, from descending orbits, (ii) the continuous GNSS position time series provided by the Nevada Geodetic Laboratory at the University of Nevada, Reno, USA (UNR-NGL) available for the investigated area, and (iii) the ONDA platform, one of the Copernicus Data and Information Access Services (DIAS). The achieved results demonstrate the capability of the proposed solution to successfully retrieve the DInSAR time series relevant to such a huge area, opening new scenarios for the analysis and interpretation of these ground deformation measurements.

We present an automatic and unsupervised tool for the systematic generation of Sentinel-1 (S1) differential synthetic aperture radar interferometry (DInSAR) coseismic products. In particular, the tool first retrieves the location, depth, and magnitude of every seismic event from interoperable online earthquake catalogs (e.g., the United States Geological Survey (USGS) and the Italian National Institute of Geophysics and Volcanology (INGV) and then, for significant (with respect to a set of selected thresholds) earthquakes, it automatically triggers the downloading of S1 data and their interferometric processing over the area affected by the earthquake. The automatic system we developed has also been implemented within a Cloud-Computing (CC) environment, specifically the Amazon Web Services, with the aim of creating a global database of DInSAR S1 coseismic products, which consist of displacement maps and the associated wrapped interferograms and spatial coherences. This information will progressively be made freely available through the European Plate Observing System (EPOS) Research Infrastructure, thus providing the scientific community with a large catalog of DInSAR data that can be helpful for investigating the dynamics of surface deformation in the seismic zones around the Earth. The developed tool can also support national and local authorities during seismic crises by quickly providing information on the surface deformation induced by earthquakes.

In this work, we investigate the Mw 5.9 earthquake occurred on 7 November 2019 in the East-Azerbaijan region, in northwestern Iran, which is inserted in the tectonic framework of the East-Azerbaijan Plateau, a complex mountain belt that contains internal major fold-and-thrust belts. We first analyze the Differential Synthetic Aperture Radar Interferometry (DInSAR) measurements obtained by processing the data collected by the Sentinel-1 constellation along ascending and descending orbits; then, we invert the achieved results through analytical modelling, in order to better constrain the geometry and characteristics of the seismogenic source. The retrieved fault model shows a rather shallow seismic structure, with a center depth at about 3 km, approximately NE–SW-striking and southeast-dipping, characterized by a left-lateral strike-slip fault mechanism (strike = 29.17°, dip = 79.29°, rake = −4.94°) and by a maximum slip of 0.80 m. By comparing the inferred fault with the already published geological structures, the retrieved solution reveals a minor fault not reported in the geological maps available in the open literature, whose kinematics is compatible with that of the surrounding structures, with the local and regional stress states and with the performed field observations. Moreover, by taking into account the surrounding geological structures reported in literature, we also use the retrieved fault model to calculate the Coulomb Failure Function at the nearby receiver faults. We show that this event may have encouraged, with a positive loading, the activation of the considered receiver faults. This is also confirmed by the distribution of the aftershocks that occurred near the considered surrounding structures. The analysis of the seismic events nucleated along the left-lateral strike-slip minor faults of the East-Azerbaijan Plateau, such as the one analyzed in this work, is essential to improve our knowledge on the seismic hazard estimation in northwestern Iran.

This paper presents a web tool for the unsupervised retrieval of Earth’s surface deformation from Synthetic Aperture Radar (SAR) satellite data. The system is based on the implementation of the Differential SAR Interferometry (DInSAR) algorithm referred to as Parallel Small BAseline Subset (P-SBAS) approach, within the Grid Processing on Demand (G-POD) environment that is a part of the ESA’s Geohazards Exploitation Platform (GEP). The developed on-demand web tool, which is specifically addressed to scientists that are non-expert in DInSAR data processing, permits to set up an efficient on-line P-SBAS processing service to produce surface deformation mean velocity maps and time series in an unsupervised manner. Such results are obtained by exploiting the available huge ERS and ENVISAT SAR data archives; moreover, the implementation of the Sentinel-1 P-SBAS processing chain is in a rather advanced status and first results are already available. Thanks to the adopted strategy to co-locate both DInSAR algorithms and computational resources close to the SAR data archives, as well as the provided capability to easily generate the DInSAR results, the presented web tool may contribute to drastically expand the user community exploiting the DInSAR products and methodologies.

We present in this work a first performance assessment of the Parallel Small BAseline Subset (P-SBAS) algorithm, for the generation of Differential Synthetic Aperture Radar (SAR) Interferometry (DInSAR) deformation maps and time series, which has been migrated to a Cloud Computing (CC) environment. In particular, we investigate the scalable performances of the P-SBAS algorithm by processing a selected ENVISAT ASAR image time series, which we use as a benchmark, and by exploiting the Amazon Web Services (AWS) CC platform. The presented analysis shows a very good match between the theoretical and experimental P-SBAS performances achieved within the CC environment. Moreover, the obtained results demonstrate that the implemented P-SBAS Cloud migration is able to process ENVISAT SAR image time series in short times (less than 7 h) and at low costs (about USD 200). The P-SBAS Cloud scalable performances are also compared to those achieved by exploiting an in-house High Performance Computing (HPC) cluster, showing that nearly no overhead is introduced by the presented Cloud solution. As a further outcome, the performed analysis allows us to identify the major bottlenecks that can hamper the P-SBAS performances within a CC environment, in the perspective of processing very huge SAR data flows such as those coming from the existing COSMO-SkyMed or the upcoming SENTINEL-1 constellation. This work represents a relevant step toward the challenging Earth Observation scenario focused on the joint exploitation of advanced DInSAR techniques and CC environments for the massive processing of Big SAR Data.

In this work, we exploited large DInSAR and GPS datasets to create a 4D image of the magma transfer processes at Mauna Loa Volcano (Island of Hawai’i) from 2005 to 2015. The datasets consist of 23 continuous GPS time series and 307 SAR images acquired from ascending and descending orbits by ENVISAT (ENV) and COSMO-SkyMed (CSK) satellites. Our results highlight how the joint use of SAR data acquired from different orbits (thus with different look angles and wavelengths), together with deformation data from GPS networks and geological information can significantly improve the constraints on the geometry and location of the sources responsible for the observed deformation. The analysis of these datasets has been performed by using an innovative method that allows building a complex source configuration. The results suggest that the deformation pattern observed from 2005 to 2015 has been controlled by three deformation sources: the ascent of magma along a conduit, the opening of a dike and the slip along the basal decollement. This confirms that the intrusion of the magma within a tabular system (rift dikes) may trigger the sliding of the SE portion of the volcanic edifice along the basal decollement. This case study confirms that it is now possible to exploit large geodetic datasets to improve our knowledge of volcano dynamics. The same approach could also be easily applied in other geodynamical contexts such as geothermal reservoirs and regions with complex tectonics.

On 16 September 2015, a Mw 8.3 interplate thrust earthquake ruptured offshore the Illapel region (Chile). Here, we perform coseismic slip fault modeling based on multi-orbit Sentinel 1-A (S1A) data. To do this, we generate ascending and descending S1A interferograms, whose combination allows us to retrieve the EW and vertical components of deformation. In particular, the EW displacement map highlights a westward displacement of about 210 cm, while the vertical map shows an uplift of about 25 cm along the coast, surrounded by a subsidence of about 20 cm. Following this analysis, we jointly invert the multi-orbit S1A interferograms by using an analytical approach to search for the coseismic fault parameters and related slip values. Most of the slip occurs northwest of the epicenter, with a maximum located in the shallowest 20 km. Finally, we refine our modeling approach by exploiting the Finite Element method, which allows us to take geological and structural complexities into account to simulate the slip along the slab curvature, the von Mises stress distribution, and the principal stress axes orientation. The von Mises stress distribution shows a close similarity to the depth distribution of the aftershock hypocenters. Likewise, the maximum principal stress orientation highlights a compressive regime in correspondence of the deeper portion of the slab and an extensional regime at its shallower segment; these findings are supported by seismological data.