Earthquake Damage Susceptibility Analysis in Barapani Shear Zone Using InSAR, Geological, and Geophysical Data

Landslides cause significant socioeconomic impacts on people and national infrastructures like railways and roads and are considered one of the common geohazards that demand more attention. In Sweden, many areas are prone to landslides due to the presence of underlying quick-clay sediments, which may lead to minor to large slides. Ground deformation monitoring in such hazardous areas is important for a better understanding of the landslide processes and mitigation of hazards. Over the last decade, Interferometric Synthetic Aperture Radar (InSAR) time-series techniques, such as Persistent Scatterer Interferometry (PSI) and the Small Baseline Subset (SBAS), have become a crucial tool for ground surface deformation analysis. SBAS and PSI use SAR data to retrieve the time-series cumulative phase of the Persistent Scatters (PS). The main objective of this study was to demonstrate the advantage of using advanced InSAR time series analysis for a better understanding of surface deformation before a landslide event. We focused on the recent landslide on the E6 Sweden-Norway highway near Stenungsund in Southern Sweden, which occurred on 23 September 2023. Sentinel-1 SAR data was collected between 2018 and September 2023, with ascending flight direction to measure the pre-event deformation in the landslide zone. We used Alaska Facility (ASF) on-demand product processes based on Hybrid Pluggable Processing Pipeline (Hyp3) to search, process, and download time series Sentinel-1 data. We also used Miami INsar Time-series software in PYthon (Mintpy) to perform cloud-based SBAS processing using unwrapped interferograms stack derived from Sentinel-1 time series data. In addition, we employed Basic PSI products (ground motion in the Line-of-Sight (LOS) direction) provided by the European Ground Motion Service (EGMS). The initial SBAS results and EGMS Basic products for the same ascending orbit showed continuous deformation on the highway segment in the landslide zone over the last EGMS update period, 2018 to 2022 for the PSI results and 2018 to 2023  for the SBAS results. The first five-year period of the EGMS Basic and Ortho products, i.e., 2015-2021, was also checked and showed the same results over the longer period between 2015 and 2021. Both sets of PSI and SBAS results agree on the annual cm-level (10-15 mm/year) subsidence rate of the highway before the landslide, with SBAS analysis yielding more measurement points, especially in the vegetated and unbuilt areas. The initial results showed that the SBAS technique could provide more information within the hazardous zone; nevertheless, due to Sentinel-1 C-band data, the quality of the results can be degraded by coherence variations in the vegetated areas.  The comparison of preliminary results of InSAR data processing and available EGMS products provides insights into ground movements, facilitating a comprehensive understanding of evolving conditions before the landslide. Nevertheless, the results emphasize the importance of incorporating advanced time series InSAR techniques for continuously monitoring infrastructures such as railroads and highways to support sustainable development and natural hazard assessments.

Glacier retreats, along with associated geomorphological and periglacial processes, can significantly impact hiking infrastructure and have consequences for the local tourism industry, which heavily depends on high-altitude mountaineering. Interferometric Synthetic Aperture Radar (InSAR) time-series techniques, such as the Small Baseline Subset (SBAS) method, have gained considerable attention for analysing surface deformation and slope instability. InSAR utilises phase information to measure time-series surface deformations with sub-centimetre accuracy.The primary objective of this study is to identify and measure surface deformation and slope instability using InSAR, and to investigate the potential impacts on selected alpine huts in high mountain regions in Austria. We use time-series Sentinel-1 data and open-source software, including the InSAR Scientific Computing Environment (ISCE) tool for SAR data processing and the Miami InSAR Time-series software in PYthon (Mintpy) for SBAS analysis. By integrating the InSAR results, slope units derived from a high-resolution digital elevation model (DEM), and alpine infrastructure locations, we identify areas showing significant deformation rates. The initial results provide insights into the slope instabilities and surface deformation that may affect alpine infrastructure. The results highlight the potential of advanced InSAR time-series analysis for monitoring surface deformation in highly dynamic alpine landscapes, where increasing natural hazards, such as landslides, necessitate improved natural hazard and risk management. Future steps include discussion and validation of the results in collaboration with experts from alpine associations.

Earth surface displacements from interferometric synthetic aperture radar (InSAR) have long been used to study deformation from a wide range of geophysical processes. Whereas deformation rates can be robustly estimated from InSAR by averaging many individual deformation observations, noise in these observations has limited their utility for generating deformation time series. In this article, we introduce a novel combination of InSAR and Global Positioning System (GPS) data that align InSAR displacements to an absolute reference and reduces long-wavelength spatial errors prior to InSAR time series construction. We test our GInSAR (GPS-enhanced InSAR) methodology on Sentinel-1 data over the southern Central Valley, CA, USA, comparing GInSAR displacement velocities and time series with those from three other referencing techniques. We find that the GInSAR approach outperforms alternative methods, yielding mm-level displacement differences with respect to collocated cGPS. By contrast, other referencing methods can overestimate peak subsidence velocities in the Central Valley by upwards of 10%, deviate by tens of millimeters relative to cGPS validation time series, and contain spatial biases absent in the GInSAR methodology. We also present a modification to the widely used small baseline subset (SBAS) technique for time series estimation, whereby we use a temporal connectedness constraint to regularize the mathematical inversion and increase the number of InSAR pixels with valid time series estimates.

In this paper, we evaluate the effectiveness of four land-deformation measurement techniques for monitoring slow-moving landslides along a high-risk section of the national railway corridor traversing the Thompson River valley, British Columbia, Canada. The geomorphically active North Slide acts as an ideal field laboratory for testing and evaluating novel monitoring techniques and methods. We compare differential processing of Structure from Motion (SfM) products such as point-cloud elevation models and orthophotos derived from Remotely Piloted Aircraft Systems (RPAS), along with satellite based Interferometric Synthetic Aperture Radar (InSAR) deformation measurements derived from RADARSAT Constellation Mission (RCM). These results are ground-truthed with periodic real-time kinematic (RTK) global navigation satellite system (GNSS) measurements. We evaluate point-cloud comparison techniques, including the multi-scale model-to-model cloud comparison (M3C2) algorithm and digital ortho image correlation techniques. Multi-temporal RCM InSAR deformation measurements are processed using a semi-automated processing system for interferogram generation and unwrapping. Manual processing of small baseline subsets (SBAS) leads to the recovery of 1-dimensional line-of-sight (LoS) and 2-dimensional deformation measurements. Lastly, we discuss the strengths and limitations of these techniques, considerations for interpreting their outputs, and considerations for direct comparisons between InSAR, RPAS and RTK-GNSS deformation measurements.

In this study, geodynamic processes in the Western Anatolia Region were analyzed using Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite Systems (GNSS) data, incorporating wavelet transformation of InSAR time series based on GNSS observations. InSAR data were processed over a six-year period to produce line of sight (LOS) displacement maps. Ascending and descending track data were merged to derive information on both horizontal and vertical displacements. The InSAR-derived displacements were then compared with GNSS station data from the region to assess variations in the east and up components obtained by both techniques. Significant horizontal and vertical displacements were detected. While the Menemen Plain experienced a subsidence of up to 15 centimeters over a six-year period, the island of Samos experienced a rise of 29 centimeters. The performance of InSAR results was evaluated against GNSS data using Root Mean Square Error (RMSE). The RMSE values significantly decreased after applying corrections to the InSAR processing, indicating improved accuracy. For the DEUG station, the RMSE between InSAR and GNSS time series improved to 1.93% in the east component and 5.29% in the vertical component after wavelet-based noise removal. At the IZMI station, the RMSE was reduced to 2.62% (east) and 6.00% (vertical). Finally, at the CES1 station, the RMSE was reduced to 2.71% for the east component and 5.84% for the vertical component, all after correction.

In the San Luis Valley (SLV), Colorado legislation passed in 2004 requires that hydraulic head levels in the confined aquifer system stay within the range experienced in the years 1978–2000. While some measurements of hydraulic head exist, greater spatial and temporal sampling would be very valuable in understanding the behavior of the system. Interferometric synthetic aperture radar (InSAR) data provide fine spatial resolution measurements of Earth surface deformation, which can be related to hydraulic head change in the confined aquifer system. However, change in cm‐scale crop structure with time leads to signal decorrelation, resulting in low quality data. Here we apply small baseline subset (SBAS) analysis to InSAR data collected from 1992 to 2001. We are able to show high levels of correlation, denoting high quality data, in areas between the center pivot irrigation circles, where the lack of water results in little surface vegetation. At three well locations we see a seasonal variation in the InSAR data that mimics the hydraulic head data. We use measured values of the elastic skeletal storage coefficient to estimate hydraulic head from the InSAR data. In general the magnitude of estimated and measured head agree to within the calculated error. However, the errors are unacceptably large due to both errors in the InSAR data and uncertainty in the measured value of the elastic skeletal storage coefficient. We conclude that InSAR is capturing the seasonal head variation, but that further research is required to obtain accurate hydraulic head estimates from the InSAR deformation measurements.

The prediction of land subsidence is a crucial step for early warning of urban infrastructure damage and timely remedy. However, the performance of most mathematical and empirical prediction models is often compromised by their large number of parameters, complex operational processes and sparsely measured values. Currently, the traditional neural network models are popular and effective, but they cannot accurately discover the characteristic changes of time series data. In this paper, a long short-term memory (LSTM) neural network was proposed to predict the land subsidence of time series Interferometric Synthetic Aperture Radar (InSAR). First, the Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique was utilized to monitor the time series land subsidence at Beijing Capital International Airport (BCIA) from 2005 to 2010 based on ENVISAT ASAR images with a descending orbit. The results were compared with the existing results to verify the reliability and then used to analyse the temporal and spatial characteristics of the time series land subsidence of the BCIA. Based on the time series InSAR deformation data, the LSTM neural network was used to establish the prediction model of time series InSAR, and the results were compared with those of the Multi-Layer Perceptron (MLP) and Recurrent Neural Network (RNN). The comparison results showed that the LSTM neural network was more accurate than the MLP and RNN on the point scale (the root mean square error was 4.60 mm and the mean absolute error was 3.18 mm), the correlation coefficients between the prediction results of the LSTM neural network and the real InSAR measurement results in 2007 and 2008 were 0.93 mm and 0.96 mm, respectively, indicating that LSTM neural network had better prediction performance. Eventually, based on the land subsidence data of time series InSAR from 2006 to 2010, the LSTM neural network was applied to predict the BCIA time series land subsidence in 2011. The results predicted that cumulative subsidence in September 2011 would reach a maximum of 350 mm. Therefore, the LSTM neural network is a potentially effective prediction method, which can replace numerical or empirical models in the absence of detailed hydrogeological data. Moreover, its prediction results can be used to assist decision-making, early warning and hazard relief.

The fusion of global navigation satellite system (GNSS) and interferometric synthetic aperture radar (InSAR) deformation data can leverage the advantages of GNSS high temporal resolution and InSAR high spatial resolution, and obtain more abundant deformation data for constraints on geophysical structural and mechanical parameters. Existing studies seldom consider the spatial heterogeneity of largescale deformation data, which easily leads to obvious spatial aggregation of errors in the results of fusion. Here, we propose a novel multiresolution segmentation fusion (MRSF) method that uses a multiresolution segmentation algorithm to automatically classify the spatial heterogeneity of InSAR deformation data with similar deformation characteristics. We applied the MRSF method to the fusion of GNSS and InSAR deformation data covering the central valley aquifer system (CVAS) in southern California to verify its precision and robustness. Results show that the MRSF method can accurately reflect spatiotemporal evolution characteristics of displacement data and reliably estimate deformation for the times and locations of missing data. We then tested this method for geophysical parameter estimation by constructing three different sets of data, including dense GNSS sites, sparse GNSS sites, and sparse GNSS sites fused with InSAR data using MRSF, to invert the slip distribution of the Cascadia subduction zone. Results show that the inverted slip of the fused InSAR and GNSS data is comparable to that of the dense GNSS sites. Therefore, the MRSF method can obtain deformation results with high precision and high spatiotemporal resolution and effectively compensate for the lack of data caused by sparse GNSS sites during the geophysical inversion process.

Detection and Measurement of Land Subsidence Using Global Positioning System Surveying and Interferometric Synthetic Aperture Radar, Coachella Valley, California, 1996-2005

Augustine volcano, located along the Aleutian Arc, is one of the most active volcanoes in Alaska and nearby islands, with seven eruptions occurring between 1812 and 2006. This study monitored the surface displacement before and after the most recent 2006 eruption. For analysis, we conducted a time-series analysis on data observed at the permanent GPS(Global Positioning System) observation stations in Augustine Island between 2005 and 2011. According to the surface displacement analysis results based on GPS data, the movement of the surface inflation at the average speed of 2.3 cm/year three months prior to the eruption has been clearly observed, with the post-eruption surface deflation at the speed of 1.6 cm/year. To compare surface displacements measurement by GPS observation, ENVISAT(Environmental satellite) radar satellite data were collected between 2003 and 2010 and processed the SBAS(Small Baseline Subset) method, one of the time-series analysis techniques using multiple InSAR(Interferometric Synthetic Aperture Radar) data sets. This result represents 0.97 correlation value between GPS and InSAR time-series surface displacements. This research has been completed precise surface deformation using GPS and time-series InSAR methods for a detection of precursor symptom on Augustine volcano.

<strong class="journal-contentHeaderColor">Abstract.</strong> Satellite-based earth observation sensors are increasingly able to monitor geophysical signals related to natural hazards, and many groups are working on rapid data acquisition, processing, and dissemination to data users with a wide range of expertise and goals. A particular challenge in the meaningful dissemination of Interferometric Synthetic Aperture Radar (InSAR) data to non-expert users is its unique differential data structure and sometimes low signal to noise ratio. In this study, we evaluate the online dissemination of ground deformation measurements from InSAR through Twitter, alongside the provision of open access InSAR data from the Centre for Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET) Looking Into Continents from Space with Synthetic Aperture Radar (LiCSAR) processing system. Our aim is to evaluate (1) who interacts with disseminated InSAR data, (2) how the data are used and (3) to discuss strategies for meaningful communication and dissemination of open InSAR data. We found that InSAR Twitter activity was primarily associated with natural hazard response, specifically following earthquakes and volcanic activity, where InSAR measurements of ground deformation were disseminated, often using wrapped and unwrapped interferograms. For earthquake events, Sentinel-1 data were acquired, processed, and tweeted within 4.7&plusmn;2.8 days (shortest was one day). Open access Sentinel-1 data dominated the InSAR tweets and were applied to volcanic and earthquake events in the most engaged with (retweeted) content. Open access InSAR data provided by LiCSAR was widely accessed, including automatically processed and tweeted interferograms and interactive event pages revealing ground deformation following earthquake events. The further work required to integrate dissemination of InSAR data into longer-term disaster risk reduction strategies is highly specific, both to hazard-type, international community of practice, and local political setting and civil protection mandates. Notably, communication of uncertainties and processing methodologies are still lacking. We conclude by outlining the future direction of COMET LiCSAR products to maximise their useability.

<strong class="journal-contentHeaderColor">Abstract.</strong> Satellite-based earth observation sensors are increasingly able to monitor geophysical signals related to natural hazards, and many groups are working on rapid data acquisition, processing, and dissemination to data users with a wide range of expertise and goals. A particular challenge in the meaningful dissemination of Interferometric Synthetic Aperture Radar (InSAR) data to non-expert users is its unique differential data structure and sometimes low signal to noise ratio. In this study, we evaluate the online dissemination of ground deformation measurements from InSAR through Twitter, alongside the provision of open access InSAR data from the Centre for Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET) Looking Into Continents from Space with Synthetic Aperture Radar (LiCSAR) processing system. Our aim is to evaluate (1) who interacts with disseminated InSAR data, (2) how the data are used and (3) to discuss strategies for meaningful communication and dissemination of open InSAR data. We found that InSAR Twitter activity was primarily associated with natural hazard response, specifically following earthquakes and volcanic activity, where InSAR measurements of ground deformation were disseminated, often using wrapped and unwrapped interferograms. For earthquake events, Sentinel-1 data were acquired, processed, and tweeted within 4.7&plusmn;2.8 days (shortest was one day). Open access Sentinel-1 data dominated the InSAR tweets and were applied to volcanic and earthquake events in the most engaged with (retweeted) content. Open access InSAR data provided by LiCSAR was widely accessed, including automatically processed and tweeted interferograms and interactive event pages revealing ground deformation following earthquake events. The further work required to integrate dissemination of InSAR data into longer-term disaster risk reduction strategies is highly specific, both to hazard-type, international community of practice, and local political setting and civil protection mandates. Notably, communication of uncertainties and processing methodologies are still lacking. We conclude by outlining the future direction of COMET LiCSAR products to maximise their useability.

<strong class="journal-contentHeaderColor">Abstract.</strong> Satellite-based earth observation sensors are increasingly able to monitor geophysical signals related to natural hazards, and many groups are working on rapid data acquisition, processing, and dissemination to data users with a wide range of expertise and goals. A particular challenge in the meaningful dissemination of Interferometric Synthetic Aperture Radar (InSAR) data to non-expert users is its unique differential data structure and sometimes low signal to noise ratio. In this study, we evaluate the online dissemination of ground deformation measurements from InSAR through Twitter, alongside the provision of open access InSAR data from the Centre for Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET) Looking Into Continents from Space with Synthetic Aperture Radar (LiCSAR) processing system. Our aim is to evaluate (1) who interacts with disseminated InSAR data, (2) how the data are used and (3) to discuss strategies for meaningful communication and dissemination of open InSAR data. We found that InSAR Twitter activity was primarily associated with natural hazard response, specifically following earthquakes and volcanic activity, where InSAR measurements of ground deformation were disseminated, often using wrapped and unwrapped interferograms. For earthquake events, Sentinel-1 data were acquired, processed, and tweeted within 4.7&plusmn;2.8 days (shortest was one day). Open access Sentinel-1 data dominated the InSAR tweets and were applied to volcanic and earthquake events in the most engaged with (retweeted) content. Open access InSAR data provided by LiCSAR was widely accessed, including automatically processed and tweeted interferograms and interactive event pages revealing ground deformation following earthquake events. The further work required to integrate dissemination of InSAR data into longer-term disaster risk reduction strategies is highly specific, both to hazard-type, international community of practice, and local political setting and civil protection mandates. Notably, communication of uncertainties and processing methodologies are still lacking. We conclude by outlining the future direction of COMET LiCSAR products to maximise their useability.

<strong class="journal-contentHeaderColor">Abstract.</strong> Satellite-based earth observation sensors are increasingly able to monitor geophysical signals related to natural hazards, and many groups are working on rapid data acquisition, processing, and dissemination to data users with a wide range of expertise and goals. A particular challenge in the meaningful dissemination of Interferometric Synthetic Aperture Radar (InSAR) data to non-expert users is its unique differential data structure and sometimes low signal to noise ratio. In this study, we evaluate the online dissemination of ground deformation measurements from InSAR through Twitter, alongside the provision of open access InSAR data from the Centre for Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET) Looking Into Continents from Space with Synthetic Aperture Radar (LiCSAR) processing system. Our aim is to evaluate (1) who interacts with disseminated InSAR data, (2) how the data are used and (3) to discuss strategies for meaningful communication and dissemination of open InSAR data. We found that InSAR Twitter activity was primarily associated with natural hazard response, specifically following earthquakes and volcanic activity, where InSAR measurements of ground deformation were disseminated, often using wrapped and unwrapped interferograms. For earthquake events, Sentinel-1 data were acquired, processed, and tweeted within 4.7&plusmn;2.8 days (shortest was one day). Open access Sentinel-1 data dominated the InSAR tweets and were applied to volcanic and earthquake events in the most engaged with (retweeted) content. Open access InSAR data provided by LiCSAR was widely accessed, including automatically processed and tweeted interferograms and interactive event pages revealing ground deformation following earthquake events. The further work required to integrate dissemination of InSAR data into longer-term disaster risk reduction strategies is highly specific, both to hazard-type, international community of practice, and local political setting and civil protection mandates. Notably, communication of uncertainties and processing methodologies are still lacking. We conclude by outlining the future direction of COMET LiCSAR products to maximise their useability.

Interferometric synthetic aperture radar (InSAR), one of the most commonly used remote sensing methods for observing and monitoring land subsidence, has been applied in Hanoi, Vietnam in several studies with results showing deformation up to 2014. However, freely accessible Sentinel-1 InSAR data have not been investigated thoroughly to date. Here, we investigate the most recent land surface deformation in Hanoi for the period 20162020 using Sentinel-1A SAR data. The analysis is conducted on 114 SAR scenes with both the Persistent Scatterer InSAR (PSInSAR) and Small BAseline Subset (SBAS) methods. The GPS-based deformation time series are used to verify InSAR results and borehole groundwater level measurements are employed to evaluate the relationship between groundwater depletion and surface subsidence. The results show that observed deformation from SBAS and PSInSAR is consistent in both spatial patterns and statistics, in which two high-rate subsiding bowls were detected in Dan Phuong/Hoai Duc and Ha Dong/Thanh Tri districts with the mean subsiding rates of ∼5 mm per year. GPS and InSAR deformation generally agree well except for the comparison at the JNAV station after 2017, which can be attributable to the local deformation detected by GPS and the average movement of a 100-m radius area captured by InSAR. An agreement in the drawdown trend between borehole groundwater and InSAR-derived deformation was found at four wells located within or in proximity to the two bowls. The declining rates of groundwater level at about 0.31 m per year were found at the two wells Q57a and Q58a located within the Dan Phuong/Hoai Duc bowl, corresponding to the surface subsidence rates found at 6–8 mm per year. The Q68a well was found to experience groundwater level declining at the highest rate of ∼0.9 m per year corresponding to the subsidence rate of ∼7 mm per year found by InSAR.