Since its inception in 2018, InSAR Norway has emerged as a pivotal tool in addressing geological hazards and advancing scientific research in Norway. Utilizing the C-band data from Sentinel-1, it provides annual comprehensive ground movement updates crucial for understanding and mitigating natural disasters and ensuring infrastructure stability across Norway's complex terrain. The service's impact is particularly pronounced in landslide mapping and permafrost studies, areas of critical importance given Norway's climatic and geological vulnerability.

InSAR Norway has been instrumental in detecting and monitoring landslide-prone areas, providing data essential for early warning systems and risk assessment. Detailed morpho-kinematic inventories have been updated nationwide to include previously undetected movements. By classifying slope movements and providing velocity data, InSAR Norway has significantly contributed to understanding the kinematics of landslides, enabling more cost-effective monitoring solutions. A recent study leveraging InSAR Norway data has statistically explored the link between permafrost and displacement rates of large unstable rock slopes (LURSs), revealing that permafrost presence significantly influences these rates and that complete thawing of permafrost can reduce or halt displacement, indicating the nuanced role of permafrost in geological hazard scenarios.

InSAR Norway's data has also shed light on the dynamics of rock glaciers and permafrost creep. Studies utilizing this data have revealed the impact of permafrost thawing on rock glacier velocities and the broader implications for landscape stability and hydrology. By providing detailed movement profiles of rock glaciers in transition from active to relict stages, InSAR Norway has offered insights into the effects of climate change on cold region dynamics.

The service's free and open data policy has been central to its success, catalyzing a wide range of research and operational applications by providing unrestricted access to high-quality, high-resolution data. This policy has facilitated a collaborative environment where academics, government agencies, and industry can innovate and develop solutions to shared challenges.

With the impending integration of L-band data from the NISAR satellite mission, InSAR Norway is poised for significant enhancements. NISAR data will augment the service's ability to monitor ground movements, particularly in vegetated areas and through seasonal changes. This integration reflects our commitment to adopting cutting-edge technology to improve the accuracy, timeliness, and applicability of geohazard monitoring and research. By fusing the strengths of C-band and L-band data, InSAR Norway will provide a more comprehensive and nuanced understanding of ground deformation processes, supporting safer, more informed decision-making in the face of Norway's dynamic and often harsh environmental conditions. InSAR Norway will continue its legacy of pioneering satellite-based monitoring, safeguarding communities, and advancing scientific understanding of geohazards.

How to cite: Dehls, J. F., Bredal, M., Penna, I., Larsen, Y., Aslan, G., Bendle, J., Böhme, M., Hermanns, R., Haugsnes, V. S., and Noel, F.: InSAR Norway: Advancing Geohazard Understanding through Wide-Area Analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4400, https://doi.org/10.5194/egusphere-egu24-4400, 2024.

The motivation for this study is to help providing a better understanding of the behavior of the earth's crust in high strain areas. High strain areas are regions of the Earth's crust, associated with tectonic plate boundaries, where the rates of ground deformation are particularly high. These areas are characterized by high seismic activity, making them of significant concern. The ability to estimate ground deformation in these regions is critical for understanding the underlying geological processes and for assessing the potential risk of future seismic events. Interferometric Synthetic Aperture Radar (InSAR) has shown great promise in delivering millimetre-scale ground displacement information over long distances across plate boundaries. In this project, we aim to globally measure ground deformation using the InSAR Persistent and Distributed Scatterer (PS/DS) technique, focusing on the regions where the second invariant of the strain is higher than 3 nanostrain per year.

Due to the large amount of data that has to be processed, we use the high-performance data analytics platform made available within the framework of the Terra_Byte project, a cooperation between the German Aerospace Center (DLR) and the Leibniz Computer Centre (LRZ). This enables us to process large volumes of data efficiently. We use the IWAP processor to apply the PS/DS technique to time-series of seven years of SAR images acquired by the Sentinel-1 mission. To improve the accuracy of our analysis and reduce the influence of ionospheric variations we use CODE total electron contents maps. The impact of solid earth tides (SETs) is limited by using the IERS 2010 convention. We use ECMWF reanalysis data to correct for tropospheric delays, which are the biggest error source and limiting factor for the interferometric performance at large distances. The influence of soil moisture and vegetation growth on distributed scatterers is limited by the full covariance matrix approach used in the interferograms generation. Finally, we calibrate and compare our results with GNSS measurements to show a detailed picture of ground deformation.

The results of this project will be publicly available on a global scale, including: velocity maps, timeseries, line-of-sight projection vectors. The product palette will allow custom calibration or 2D decomposition by the user. Possible applications are: the large coverage and homogeneous processing characteristics of the data could serve as a baseline reference or comparison for other studies. Geoscientists will be able to use the deformation measurements to gain a better understanding of geological processes, with the dense PS/DS measurements filling in the gaps between existing GNSS survey data, contributing to the advancement of scientific knowledge in this field.

How to cite: Gomba, G., De Zan, F., Brcic, R., and Eineder, M.: Mapping Worldwide Ground Deformation in High-Strain Areas with SAR PS/DS Interferometry and Sentinel-1 Imagery, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8645, https://doi.org/10.5194/egusphere-egu24-8645, 2024.

Spaceborne Differential Synthetic Aperture Radar Interferometry (DInSAR) represents a well-established technique to accurately retrieving ground surface displacements over large areas of the Earth, in both natural and anthropogenic hazard scenarios, with limited costs and with a centimeter to millimeter accuracy. However, the DInSAR technique retrieval capability may be affected by the so-called “temporal decorrelation phenomena” due to possible temporal changes of the imaged scene electromagnetic response. In this regard, the low-frequency SAR sensors, as those operating at the L-band, characterized by a significantly larger wavelength (~23 cm) with respect to the X-band (~3 cm wavelength) and C-band (~5.6 cm wavelength) ones, are particularly suited to mitigate the above-mentioned decorrelation effects, thanks to their capacity of maintaining the interferometric coherence for a long period. Moreover, these L-band SAR systems also imply considerable robustness with respect to the possible occurrence of phase unwrapping errors. These peculiarities have pushed the worldwide space agencies to invest in the development of L-band spaceborne SAR sensors as, for instance, the NISAR mission, jointly developed by NASA and ISRO, the PALSAR-3 mission of JAXA and the ROSE-L mission developed by ESA, as well as the already operative SAOCOM-1 sensors of CONAE. In this work, we focus on the Argentinean SAOCOM-1 constellation which is composed of two twins, full-polarimetric L-band SAR sensors. This system guarantees, over a large part of Europe (with a priority given to the Italian territory coverage), a systematic, DInSAR-oriented acquisition plan of SAR images in the StripMap mode, with a revisit time varying among 16, 24 and 48 days, in order to avoid coverage gaps. Moreover, we largely exploit the Parallel Small BAseline Subset (P-SBAS) approach, which is an advanced DInSAR method that allows us to effectively and efficiently generate displacement time-series with sub-centimeter accuracy. The capability of the P-SBAS algorithm to retrieve C- and X-band DInSAR time-series, relevant to both natural and anthropogenic hazard scenarios has already been widely demonstrated, as well as its capacity to perform analyses at different spatial resolution scales. Accordingly, we present here the results of the L-band SAOCOM-1 P-SBAS analysis carried out at medium spatial resolution (about 30 m) in different ground deformation scenarios affecting the Italian territory. In particular, the presented results are relevant to a portion of the Tuscany region (central Italy), which is affected by significant landslide phenomena. Moreover, we also consider the volcanic contexts of the Campi Flegrei caldera, Mount Etna and Stromboli island, all located in southern Italy. In this case, we fully benefit from the availability of GNSS measurements to provide a quantitative assessment of the retrieved L-band deformation time-series.

Finally, some SAOCOM-1 results, achieved by applying the full resolution P-SBAS approach over the urban areas of Rome and Naples municipalities, are also presented. Such a full spatial resolution (about 5 m of pixel size) analysis allows us to investigate the potentialities of the L-band data to overcome some of the limitations of the current high resolution X-band SAR systems in urbanized scenarios.

How to cite: Roa, Y. L. B., De Luca, C., Bonano, M., Casu, F., Franzese, M., Manunta, M., Onorato, G., Striano, P., Yasir, M., and Lanari, R.: A quantitative assessment of the SAOCOM-1 L-band DInSAR time-series retrieved through the P-SBAS approach in natural and anthropogenic hazard scenarios of the Italian territory, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16229, https://doi.org/10.5194/egusphere-egu24-16229, 2024.

Hundreds of large cities worldwide are sinking; and this will get worse as by 2050 when almost 70% of the world’s population is set to live in megalopolises, the majority of these are in low lying coastal areas. At the same time sea levels are rising. According to the World Economic Forum, several of the globe’s cities, including New York, Dhaka, London and Bordeaux, could be partially or totally submerged by 2050-2100. As a city grows the environment is put under additional pressure and this often leads to subsidence. land less suitable for building upon is developed, in low lying coastal regions these areas are often poorly consolidated recent superficial deposits. Loading of such deposits causes consolidation which adds to subsidence resulting from increased groundwater abstraction required for industry and to support a growing population.

In order to mitigate against the effects of subsidence it is imperative to understand the subsidence; its location, magnitude, timing and crucially the underlying cause. InSAR offers the ability to understand the spatial extent, magnitude and timing and when integrated with in-situ data the cause can be determined. However, this interpretation process can take a significant amount of time. With the advent of continental scale InSAR data, such as the European Ground Motion Service, and automated online processing facilities such as COMETS LICSBAS system InSAR data is becoming far easier to access. This means huge volumes of data are generated and therefore automated methods are required to extract not only the areas of ground motion but also to indicate the underlying cause of the motion.

To this end we have been using integrated time series of optical and InSAR data for areas of rapid urban growth to understand the cause of subsidence. Combination of interpretations with expected patterns of subsidence derived from models of groundwater abstraction and ground loading allow us to separate subsidence signals from these causational factors. In turn this enables the generation of characteristic time series of subsidence that we would expect to see as a result of each process. Such characteristic time series form libraries that will be the basis for machine learning to automatically interpret the InSAR data.

We will present the creation of such subsidence libraries and illustrate the process with examples from Hanoi, Kuala Lumpur and Bandung. We will also present the machine learning method where a fully automated approach using Seasonal and Trend decomposition allows trends (such as stable, linear subsidence, non-linear subsidence and seasonal) within the InSAR time series to be identified and grouped into common trend behaviours. Metrics based on derived trends also allow the ‘strength’ of certain components (such as seasonal signals) to be automatically assessed.

How to cite: Bateson, L., Gonzalez Alvarez, I., Arnhardt, R., Fleming, C., Hussain, E., Jones, L., Novellino, A., and Smith, K.: Developing Machine Learning tools for the automatic interpretation of InSAR data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14908, https://doi.org/10.5194/egusphere-egu24-14908, 2024.

The growing accessibility of multi-sensor Persistent Scatterer (PS) data in the advent of the European Ground Motion Service offers a well-established methodology for detecting and monitoring ground displacement over extended areas with sub-centimetric precision. The detection of ground deformation phenomena relies on the available PS density, which is influenced by the sensor resolution and specific site characteristics, such as the presence of stable natural and artificial reflectors. This study proposes a novel Data Fusion (DF) approach that integrates the displacement along the line of sight of PS products to unleash the full potential of multi-sensor combinations by synthesizing multi-band displacement information. The DF approach, developed by NHAZCA S.r.l. and the Research Center “CERI - Centro di Ricerca Previsione e Prevenzione dei Rischi Geologici” of the Sapienza University of Rome in the frame of the “MUSAR” project funded by ASI (Italian Space Agency), overcomes the limitations associated with individual sensor data, allowing for improved information content and data coverage.

The method based on the strain tensor approach combines data with different orbital geometries (i.e., ascending and descending) to obtain a comprehensive deformation map by extracting synthetic measurement points called Ground Deformation Markers. In our analysis, we applied, tested, and validated the fusion method in the Basilicata region of southern Italy, combining data extracted from the C-band Sentinel-1 Copernicus initiative and the COSMO-SkyMed constellation in X-band. We evaluated the DF performance within a test site characterized by homogeneous spatial and velocity PS data distribution. The method accuracy was assessed by comparing its interpolation capabilities to estimate the velocity of deformation at a specific location with those estimated by widely used traditional (i.e., linear interpolation, cubic spline interpolation, and inverse distance weighting) and advanced techniques (i.e., universal kriging and k-nearest neighbors). The predictions of interpolators were compared with randomly extracted ground truth datasets given by the observed PS velocities. The evaluation took into consideration several metrics, such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R-squared). After validating the DF application, we compared multi-sensor results with single-sensor PS to assess the capability of the method to improve spatial coverage and information content, enabling a more comprehensive understanding of ground displacements. The results verified the capabilities and robustness of the DF approach and underscored its efficacy in enhancing the accuracy and spatial coverage of ground deformation monitoring. The proposed study highlighted the DF approach as a valuable tool in geospatial analysis and satellite monitoring applications.

How to cite: Masciulli, C., Berardo, G., Stefanini, C. A., Gaeta, M., Giraldo Manrique, S., Belcecchi, N., Bozzano, F., Scarascia Mugnozza, G., and Mazzanti, P.: A Novelty Data Fusion Approach for Integrating Multi-Band/Multi-Sensor Persistent Scatterers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-354, https://doi.org/10.5194/egusphere-egu24-354, 2024.

Ground deformation, encompassing sudden and gradual shifts in the Earth's surface, poses significant global geohazard risks. These phenomena demand thorough investigation and monitoring and are influenced by a range of natural and anthropogenic factors such as mining, excessive groundwater extraction, seismic activities, structural loads, and subsurface geology. Our research is centered on the location in the Venetian-Friulian Plain (Veneto Region, NE Italy). This area is of interest because it represents a transitional zone where sedimentary deposits from both river systems (fluvial) and lagoon/coastal environments are found, marking the transition from the alluvial plain to the coastal plain. Ground displacement maps are generated using pre-event data from the Veneto Region Sentinel 1-PS data Service and the European Ground Motion Service (EGMS), allowing us to analyze the heightened susceptibility of areas undergoing deformation. Our approach integrates artificial intelligence techniques with InSAR-derived data to create comprehensive pre- and post-event multi-temporal deformation inventories and susceptibility maps. This fusion offers exceptional accuracy and timeliness in identifying, modeling, and predicting ground deformation events. Utilizing insights from InSAR data and AI techniques, we aim to project future trends and potential risks, contributing valuable insights to geohazard assessment and management within the study region.

How to cite: Khan, J., Rosi, A., Meena, S. R., and Floris, M.: The Role of Artificial Intelligence in Modeling and Predicting Ground Deformation Using Advanced InSAR Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13026, https://doi.org/10.5194/egusphere-egu24-13026, 2024.

In recent years, satellite-SAR interferometry has established itself as a widely used global monitoring technique, enabling timely detection, monitoring and mitigation of both natural disasters and human-induced ground movements. When dealing with large multi-temporal InSAR data, the ground deformation detection becomes a fundamental and complex task with the consequent pressing need to establish new approaches and tools for effectively analysing large interferometric datasets. The advanced capabilities of the satellite systems and the continuously updated processing techniques provide unprecedented amounts of data to analyse the ground deformation processes for large territories in reduced time frames. Within this context, the fast detection and characterization of the ground deformation processes constitute a milestone for what concerns the correct management and mitigation of their impact on vulnerable populations and infrastructures. As a result, the starting point for all ground deformation detection and monitoring techniques is to work with updated inventories, a fundamental yet often overlooked issue in most countries such as Great Britain. Despite the availability of a national landslide database, less than half of the landslides reported are mapped as polygons, and their state of activity is unknown. In this regard, in this work we updated the national landslide inventory by mapping new events or simply identifying their current condition of motions through the use of the data freely provided by the European Ground Motion Service (EGMS), which represents an unprecedented baseline for ground deformation applications at continental, national and local level with millimetre accuracy. The approach relies on a semi-automatic tool, recently developed at the Centre Tecnològic de Telecomunicacions de Catalunya to identify the Active Deformation Areas (ADAs). Following an initial analysis of the InSAR data, the tool allows the identification of unstable areas characterized by a minimum number of persistent scatterers with velocity values over a specific threshold. The results consist of two ADA maps corresponding to the two Sentinel-1 velocity components and, subsequently, the output can be combined with landcover and topographic maps. The study has been carried out by exploiting the horizontal and vertical velocity maps provided by the EGMS which has enabled a national-scale analysis. Subsequent steps involve the classification and temporal analysis of the identified ADAs, followed by the analysis of more relevant local case studies. 

How to cite: Medici, C., Novellino, A., Dashwood, C., and Bianchini, S.: Semi-automatic analysis of InSAR large datasets for landslide mapping and monitoring: the Great Britain case study, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6191, https://doi.org/10.5194/egusphere-egu24-6191, 2024.

Coastal Subsidence is a complex phenomenon with large spatiotemporal variability to natural processes and anthropogenic activities, leading to the potential inundation risk of major coastal cities worldwide with the increase in relative sea level rise and shoreline sinking (Shirzaei et al., 2021). India’s coastal metro-politician cities are vulnerable to future inundation risk due to the rising sea level. Kerala, a southern state in India with about 590 km of coastline covering vast habitats of rich biodiversity and occupants, has faced the impacts of coastal subsidence for the past few decades. There is a wide scope of detail investigating Kerala’s coastal land subsidence and its impact due to the rise in sea level using geodetic techniques. In this context, we explored the data from Sentinel-1 of ESA acquired along the descending track (Path 63 and 165) with a VV polarization for monitoring subsidence. The Vertical Land Motion (VLM) is analyzed using the Small BAseline Subset (SBAS) based MT-InSAR technique along the entire coastline of Kerala which is spread across 590 km (Berardino et al., 2002). A total of 1443 interferograms were generated by co-registering 326 single-look complex images and applying a spatial baseline threshold of 85 m and a temporal baseline threshold of 65 days. The result shows that most regions in Kerala are subsiding at a rate of > 5mm/year, with the Kuttanad region of Alappuzha showing a maximum subsidence of > 20mm/year. The tide gauge station of Kochi Willingdon Island shows a relative sea level trend of 1.97±mm/year. NASA’s Intergovernmental Panel on Climate Change (IPCC) AR6 report has projected a future sea level change of 0.71 meters by 2100, considering the socioeconomic scenario SSP3-7.0. The InSAR-derived VLM, projection data of sea level from the IPCC-AR6 report, and the high spatial resolution of the Digital Elevation Model (DEM) have been incorporated to map the low-lying fast subsiding zones that are prone to future flood inundation due to relative sea level rise for all the 14 districts of Kerala. We further aim to incorporate a U-net-based deep learning model to efficiently handle these terabytes of data and develop more accurate inundation maps. This study will be helpful for policymakers to take precautionary measures to prevent future inundation hazards over the shoreline. 

References

Shirzaei, M., et al. (2021). "Measuring, modelling and projecting coastal land subsidence." Nature Reviews Earth, Environment 2(1): 40-58.

Berardino, P., G. Fornaro, R. Lanari, and E. Sansosti, (2002). "A New Algorithm for Surface Deformation Monitoring Based on Small Baseline Differential SAR Interferograms." IEEE Transactions on Geoscience and Remote Sensing 40 (11): 2375–83. https://doi.org/10.1109/TGRS.2002.803792.

How to cite: Raman, A. and Ojha, C.: Impact analysis of Relative Sea Level Rise in the entire Kerala Coast of India using MT-InSAR Technique, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1130, https://doi.org/10.5194/egusphere-egu24-1130, 2024.

Underground gas storage (UGS) is of strategic importance both in terms of security of supply and to ensure the operational continuity of primary industrial basins. UGS reservoirs make it possible to guarantee the country a continuous and reliable supply of natural gas. It is well known that UGS activities can induce ground deformations, in response to gas injection and extraction cycles. The Lombardy region (Italy) has a predominant part in the Italian national policy of UGS in depleted reservoirs. In this work, five UGS reservoirs located in Lombardy and three additional ones in Italy, which differ in geometric and geo-lithological features, were considered.

In this context, the InSAR (Interferometric Synthetic Aperture Radar) technique plays a key role in monitoring ground deformations induced by UGS activities, providing precise measurements of ground displacement.

In this contribution, we present (i) an application of a multi-method approach for the analysis of trends and seasonal signals in the EGMS InSAR time series of ground displacements in the proximity of UGS reservoirs to recognise specific footprints and spatial-temporal patterns of ground deformation. For this purpose, large datasets of ground displacements covering the UGS area in Lombardy (25 km²) from 2015 to 2022 were analysed; and (ii) an interpretation of the possible causal relationship between displacement and gas injection and extraction time series using cross-correlation approach and wavelet tools in the time-frequency domain.

The multi-method approach involves the application and optimization of Principal Component (PCA) and Independent Component Analyses (ICA) in temporal (T-) and spatial (S-) modes on both ascending and descending InSAR time series, as well as on the vertical and horizontal ones, allowing for a spatial-temporal separation of the original data into a set of limited components. Among them, it is possible to isolate those related to the USG deformations, from other signals typical of the region. Subsequently, clustering analysis is performed to group the InSAR time series and identify characteristic ground deformation patterns, which could also be related to differences in grain size properties.

As a result, it was possible to recognize and separate a limited number of signal components, describing long-term displacement and seasonal fluctuations, and the derived maps allowed the characterization of the area of influence relative to each UGS reservoir. Finally, cross-correlation approach and wavelet tools made it possible to identify and interpret the time lag between the peaks and, consequently to improve the correlation between displacements and anthropogenic triggers.

To validate the deformation patterns resulting from the approach, numerical analyses were performed in which the gas injection and extraction time series were considered as input variables. 

How to cite: Rigamonti, S., Dattola, G., Ciantia, M. O., and Crosta, G. B.: A multivariate time series analysis of underground gas storage deformations using InSAR data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19174, https://doi.org/10.5194/egusphere-egu24-19174, 2024.

The importance of natural gas in meeting human energy needs persists, even amid the ongoing global energy transition. Fossil fuels, including coal, oil, biomass, and natural gas, remain central for providing essential energy services such as cooking, heating, and electricity generation for homes and businesses. Despite the increasing emphasis on renewable energy sources, total reliance on electricity is not yet feasible, necessitating a transitional phase wherein natural gas will play a key role. Natural gas, particularly in the form of methane (CH₄), remains an indispensable resource due to its efficiency and versatility. This is particularly evident during periods of increased energy demand, such as the winter season, when natural gas serves as a reliable source for heating and power generation. The importance of a steady and uninterrupted supply of natural gas is highlighted by the challenges posed by seasonal fluctuations in demand. In response to the dynamic energy landscape, Underground Gas Storage (UGS) facilities have gained prominence as a strategic solution. With 160 active projects in Europe at the end of 2021, UGS activities provide the flexibility to store and deliver natural gas continuously, adapting to daily and seasonal fluctuations. This adaptability is critical to maintain a stable energy supply, especially during peak demand periods. On the other hand, UGS cycles have the potential to induce three-dimensional deformations within the affected reservoir that are subsequently transmitted to the surface. These deformations should be monitored since they can compromise the integrity of wells and nearby infrastructure. In this context, Interferometric Synthetic Aperture Radar (InSAR) is emerging as a valuable tool for continuous monitoring ground displacement resulting from UGS activities. InSAR analysis can provide millimetre-precision measurement points, overcoming the spatial coverage of in-situ instruments. The Yela site exploits a fractured aquifer reservoir located in the Madrid Basin (Spain) currently employed for UGS activities by Enagás, the Spanish main Transmission System Operator. A correlation between long-term records of gas volume (2019-2022) with vertical and horizontal (E-W) ground displacement data (2015-2020) from the European Ground Motion Service (EGMS) and the UGS activity rates can be established. The temporal evolution of vertical ground displacement shows a clear sinusoidal signal aligned with the amplitude and periodicity of the load/discharge curve of natural gas in the reservoir. This result highlights the versatility of the InSAR approach for UGS monitoring, complementing in-situ data, enhancing safety and improving facility management. In addition, InSAR technology can allow continuous monitoring analysis for detecting changes in the UGS environment, for risk management purposes and calibration of geomechanical models useful for estimating maximum pressure values. This work introduces a replicable approach to investigate freely available ground movement data, presenting a comprehensive comparison of InSAR results for the Yela UGS site. Leveraging open-source and easily accessible data, the study offers insights into the volumetric variation model and it identifies a significant correlation between natural gas injection/withdrawal rates and InSAR ground displacement over time.

How to cite: Fibbi, G., Beni, T., Del Soldato, M., and Fanti, R.: EGMS insights into ground deformation patterns in Underground Gas Storage (UGS) activities, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8610, https://doi.org/10.5194/egusphere-egu24-8610, 2024.