High-frequency InSAR displacement monitoring and trend detection analysis

Remote sensing data, and particularly space-based interferometric synthetic aperture radar (InSAR), is being integrated into many monitoring programs for tunneling projects. Interferometry is a remote sensing technique employed to detect and track deformation of the ground’s surface over time (Gabriel et al., 1989; Massonnet and Feigl, 1998; Bamler and Hartl, 1998; Rosen et al., 2000). The technique provides regional-scale data on ground settlement with millimeter accuracy, which allow it to be analyzed alongside ground leveling and other conventional survey techniques. FIG. 1 An illustration showing the relationship between ground displacement and signal phase shift. This is the basic principle of InSAR for measuring ground movement. Early InSAR techniques were applied to study ground settlement in cities shortly after the launch of the first synthetic aperture radar (SAR) satellite in the early 1990s (Solari et al., 2018; Ferretti et al., 2006) and since the early 2000s, multitemporal stacks of SAR imagery have been processed with advanced techniques to track ground deformation as often as every new satellite passes. Frequent monitoring updates have generated a requirement for new methods of data management and analysis given the increasing size of InSAR datasets. Widely adopted industry guidelines, such as the “ITAtech Guidelines for Remote Measurements Monitoring Systems” (Schneider et al., 2015), give recommendations on the inclusion of earth observation and other remote sensing techniques in geotechnical programs for tunneling projects. The birds-eye view of the alignment, its surroundings and the greater region allow InSAR to complement measurements collected by in situ instrumentation such as GPS stations, extensometers and total stations. This article offers a short overview of InSAR technology and focuses on recent advances in techniques for identifying and categorizing key areas in InSAR datasets over urban tunneling projects. The future of SAR satellite missions and their impact on InSAR technology for tunneling projects is also discussed. InSAR technology Satellites equipped with SAR sensors continuously circumnavigate the earth on a polar orbit and acquire images of the Earth’s surface by emitting electromagnetic waves and recording the reflected signal. Basic Differential InSAR (D-InSAR) techniques compare two SAR images acquired at different times over the same area. The difference in phase between these two images is related to the displacement of the radar target along the direction of the sensor-target line-of-sight (LOS) (Fig. 1). FIG. 2 Low-resolution Sentinel-1 data (left) and high-resolution TerraSAR-X data (right) over a same urban area. The displacement patterns are in agreement despite the difference in measurement point density. The emergence of advanced D-InSAR (A-D-InSAR) techniques in the late 1990s, allowed for processing multiple images acquired over the same area, providing the ability to estimate and remove the largest sources of noise affecting D-InSAR outputs, including signal traveltime delays caused by the atmosphere. This provided more accurate displacement measurements, with a precision in the order of less than a millimeter. Persistent scatterer interferometry, the first A-D-InSAR technique, identifies point-wise permanent scatterers (PS), allowing for pixels that show both consistent amplitude and coherent phase in the entire stack of analyzed images to be monitored over time (Ferretti et al. 2000, 2001). PS, or natural radar targets, are typically manmade structures (buildings, streetlights, transmission towers, etc.) as well as rocks, bare surfaces, and linear structures such as roads or bridges that can reflect a signal back to the satellite. FIG. 3 SqueeSAR results over a railway tunnel in the Alps (Italy). The preliminary planned entrance of the tunnel was modified to avoid an area with evidences of active slope movements. With the aim of increasing measurement point density in rural areas with less infrastructure and the presence of some vegetation, a new technique known as SqueeSAR was presented by Ferretti et al. in 2011. This technique supplements the PS approach by obtaining information from distributed scatterers (DS). Together, the PS and DS measurements provide a network of radar benchmarks, much like a global positioning system (GPS) network. Such datasets can be employed to monitor the displacement of individual buildings or infrastructures as well as entire cities, regions or states. FIG. 4 SqueeSAR monitoring during the contruction of an underground high-speed rail station in a historical city center. The removal of pre-existent masonry buildings on the surface caused the ground surface to rebound, with movement extending beyond the area monitored by the insitu instrumentation (leveling benchmarks and total stations). FIG. 5 Ground deformation maps showing the results of the high-frequency SqueeSAR monitoring service currently being provided to the regional government of Tuscany, Italy using data from two satellite orbits. (Source: Raspini et al., 2018) There are many low-resolution SAR image archives going back to the early 1990s that are now in the public domain, making InSAR highly accessible and leading to its extensive use. Since the mid-2000s, several commercially operated satellites have collected high- resolution imagery, contributing to spatially dense InSAR data over many tunneling projects. In urban areas, InSAR provides a density of thousands of measurement points per square kilometer (Fig. 2). With the increased frequency of satellite data availability and new developments to processing algorithms, it is possible to receive highly precise ground displacement data updates with each new image acquisition (Raspini et al., 2018). FIG. 6 High-frequency monitoring results in Tuscany. The anomalies are color-coded according to the cause of displacement: slope instability, subsidence, geothermal activity or uplift. (Source: Raspini et al., 2018) InSAR monitoring of tunnels Many contemporary geotechnical monitoring programs for tunnel excavation projects now incorporate InSAR data. The synoptic, birds-eye view provided by the highdensity point cloud output typically complements a selection of total stations, GPS and other instrumentation installed at discrete locations. The measurement density of InSAR datasets depends on the natural reflectivity of the ground, and in urban environments, buildings and infrastructure act as strong reflectors of the satellite’s radar signal. SAR data have been collected for more than two decades, and large archives exist in many parts of the world, thus providing an opportunity to study historical ground deformation since the early 1990s. At the tunnel planning stage, this is often used to examine pre-existing ground deformation near the proposed alignment. In the case of the railway from Venice to Trieste in Italy, historical InSAR data were used to characterize landslides along the proposed alignment. Modifications to the proposed alignment were made based on the detection of an active landslide (Fig 3). Once tunnel construction is underway, InSAR is employed to complement the in situ monitoring instrumentation. It offers wide-area coverage beyond the tunnel alignment, potentially capturing displacement induced by the tunnel excavation, dewatering or reactivated slope instabilities. These types of movement can extend for hundreds of meters or even kilometers from the project area. An example of ground settlement more than one city block from a tunnel excavation in a historical city center in Europe is shown in Fig. 4. Once excavations have been completed, InSAR is employed as a longer-term monitoring tool for periodic checks of ground stability, supporting structural health monitoring and providing early warnings of changes in settlement rates. High-frequency updates Significant advances to data processing algorithms, coupled with cloud computing, can now produce updated measurements within hours of each new satellite image acquisition. With new image acquisitions occurring every six to 12 days from a single satellite, the combined use of multiple satellites can provide near-daily revisits. This increased image frequency, along with major advances to InSAR processing algorithms and the advent of cloud computing have made it possible for updated measurements to become available the same day that a satellite image is acquired. These advances in delivery speed and frequency have brought new challenges related to the management of large amounts of datasets and the extraction of relevant information. Every update typically includes hundreds of thousands to millions of measurement points in urban environments, meaning that it is necessary to design tools to identify and highlight critical information in a timely manner. FIG. 7 Example of high-frequency monitoring coupled with trend detection analysis over a tunnel excavation in an urban enviroment. Updated ground deformation maps are provided every 11 days and the trend variation analysis is set to Δv ≥ 3 mm/yr. FIG. 8 Examples of time series flagged by the trend detection tool. Both points show a change in the deformation rate in the last 90 days. The regional government of Tuscany, in central Italy, was one of the first public administrations to implement the use of a high-frequency InSAR service. Figures 5 and 6 show time series of displacement that are continuously updated with every new image acquisition from the Sentinel-1 satellite, every 12 days. These advances in delivery speed and frequency have brought new challenges for timely data interpretation and implementation, particularly in urban environments, where datasets can contain millions of measurement points. A method for identifying and highlighting key areas quickly is critical. Deformation trend variation algorithm The development of a deformation trend variation tool designed to rapidly examine millions of points, and flag only those where changes in ground deformation trends have occurred, has become an integral part of risk mitigation procedures in high-frequency monitoring. The tool triggers an alarm that draws the attention of the stakeholder and potentially activates further investigation. The deformation trend variation algorithm uses a series of parameters and movement thresholds that are application dependent and customized to the project, and are continuously updated and refined throughout the life of the project. Urban environments are particularly sensitive to risks and constraints associated with tunneling activities due to the high density of elements at risk and public safety requirements. Even relatively small displacements need to be flagged, thus necessitating very low sensitivity thresholds for the trend variation tool, typically in the range of 2 to 4 mm, within a one- to three-month period. Figure 7 shows an example of a current high-frequency monitoring service for a twin tunnel excavation. The service uses high-resolution satellite imagery and provides updated deformation time series for close to 1 million measurement points every 11 days. The service is coupled with an automated trend variation analysis to automatically identify and flag measurement points with a change of at least 3 mm/yr in the displacement rate (Fig. 8). The output highlights areas of acceleration (pink circles) over the entire tunnel alignment and is compared to the localized in situ monitoring results. The future of InSAR monitoring Advances in SAR technology have seen a drastic reduction in the size of satellites along with improvements in their capabilities. Early SAR satellites weighed in the order of tons while the new satellites being developed weigh tens of kilograms, making them much more affordable to launch. Furthermore, while previous satellites were mainly owned and operated by national space agencies, new private startups have started designing constellations of satellites that may soon revolutionize SAR remote sensing. The next few years will see the advent of hundreds of so-called “nano-satellites” orbiting the earth that will provide SAR imagery on a daily and even hourly basis, allowing near-real time, satellite-based ground displacement monitoring, coming closer to mirroring the data update frequencies of other geotechnical monitoring methods. Conclusions Surface deformation monitoring is a key component in the identification and mitigation of risks related to tunnel excavation. Space-based InSAR is now being regularly included into comprehensive geotechnical monitoring programs for tunneling projects as it offers a synoptic, wide-area view that, coupled with localized, sparse in situ real-time systems, offers a comprehensive solution for spatially and temporally dense ground deformation data. Ground deformation updates can now be available within hours of each new satellite image acquisition. The advent of high-repeat updates has spawned the development of sophisticated deformation trend variation tools to sift through very large, spatially dense datasets and immediately identify critical areas of concern. Future SAR satellite missions will give the geotechnical monitoring community access to daily and hourly InSAR ground deformation data, placing even more importance on trend variation tools that can analyze and highlight critical areas during tunnel excavation work. References Bamler, R. and Hartl, P. (1998). Synthetic Aperture Radar Interferometry. Inverse Problems, 14, R1–R54. Bischoff, C.A., Basilico, M., Ferretti, A., Molinaro, D., Giannico, C., Ghail, R.C., Mason, P.J. (2017). A comparison between TerraSAR-X and Sentinel- 1 PSInSAR data for infrastructure monitoring in London (UK). GRSG 28th International Annual Conference “Applied Geological Remote Sensing”, 13-15 December 2017, Lisbon (Portugal) Ferretti, A., Prati, C. and Rocca, F. (2000). Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 38(5), 2202–2212. Ferretti, A., Prati, C. and Rocca, F. (2001). Permanent Scatterers in SAR Interferometry. IEEE Transactions on Geoscience and Remote Sensing, 39(1), 8–20. Ferretti, A., Prati, C., Rocca, F. and Wasowski, J. (2006). Satellite interferometry for monitoring ground deformations in the urban environment. 10th International IAEG Congress, 6-10 September, Nottingham (United Kingdom). Ferretti, A., Fumagalli, A., Novali, F., Prati, C., Rocca, F. and Rucci, A. (2011). A new algorithm for processing interferometric data-stacks: SqueeSAR. IEEE Transactions on Geoscience and Remote Sensing, 49(9), 3460–3470. Gabriel, A.K, Goldstein, R.M. and Zebker, H.A. (1989). Mapping small elevation changes over large areas: differential radar interferometry. Journal of Geophysical Research, 94, 9183–9191 Massonnet, D. and Feigl, K.L. (1998). Radar Interferometry and Its Application to Changes in the Earth’s surface. Reviews of Geophysics, 36(4), 441–500. Raspini, F., Bianchini, S., Ciampalini, A., Del Soldato, M., Solari, L., Novali, F., Del Conte, S., Rucci, A., Ferretti, A., Casagli, C. (2018). Continuous, semi-automatic monitoring of ground deformation using Sentinel-1 satellites. Scientific Reports 2018, 8, 7253. Rosen, P., Hensley, S., Joughin, I., Li, F., Madsen, S.N., Rodriguez, E., Goldstein, R. (2000) Synthetic Aperture Radar Interferometry. Proceedings of the IEEE, 88(3), 333–382. Schneider, O., Ag, A.T., Leader, S., Beth, M., Sa, S., Edelmann, T. & Ag, H. 2015. ITA Tech Guidelines for Remote Measurements Monitoring Systems ITAtech Activity Group Monitoring. Solari, L., Del Soldato, M., Bianchini, S., Ciampalini, A., Ezquerro, P., Montalti, R., Raspini, F. and Moretti, S. From ERS 1/2 to Sentinel-1: Subsidence Monitoring in Italy in the Last Two Decades. Front. Earth Sci. 6:149. doi: 10.3389/feart.2018.00149

High-frequency InSAR displacement monitoring and trend detection analysis

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