On 6 February 2023, two large earthquakes (moment magnitude 7.8 and 7.6) shocked a vast area of southeastern Türkiye and northern Syria, leading to heavy casualties and economic loss. To investigate the rupture process over multiple fault segments, we performed a comprehensive analysis of local seismic and geodetic data and determined supershear ruptures on the initial branch and the Pazarcık and Erkenek segments and subshear ruptures on the Amanos segment of event 1. The bilateral rupture of event 2 also presents distinct sub- and supershear velocities. The dynamic stress of the branch fault rupture triggered the Pazarcık segment initial rupture at a point 9 kilometers west of the junction of these two faults, boosting the supershear rupture of the Pazarcık segment of the main fault. The geometry and prestress level of multiple segments controlled the rupture behaviors and influenced the ground shaking intensity.

Infrastructure monitoring can facilitate the evaluation of capacity and functionality loss and therefore provide valuable data and evidence for reliable, lifetime and automated risk assessments. Terrestrial monitoring systems can provide information about assets and their condition. Yet, they fall short of engineering requirements related to the history of the asset loading, deterioration and potential damage, which is of paramount importance in risk-based decision-making by infrastructure operators. This capability gap can be filled by Persistent Scatterer Interferometry (PSI) Monitoring in conjunction with engineering forensics and judgment. This paper puts forward this hybrid approach to assess the structural condition of a landmark bridge by coupling advanced Differential SAR Tomography (D-TomoSAR) techniques with an adaptive deformation model, environmental measurements and engineering evidence and evaluations. A dataset of 360 Sentinel A &B images covering the period between 10/2014 and 2/2020 and temperature records to account for seasonal deformation were deployed. Displacement products, such as the development of annual displacement and time series were calculated demonstrating a strong correlation with temperature variation and resulting deformations of the asset. The spatiotemporally evolving deformation trends of the bridge superstructure were derived, and vulnerable deck locations were identified on the basis of engineering forensics, judgment and progressive displacements calculated from the D-TomoSAR technique. The vertical displacement profile at the critical deck location was developed reaching a displacement velocity of 1.8 mm/year and potential deterioration issues are manifested as potential causes of the observed geometric alterations. Based on the D-TomoSAR results matched with engineering evidence and on-site visual inspections and judgment, this study contributed to the decision-making process. This research is a pilot showcase of D-TomoSAR for the bridge monitoring, not only providing mm-level quantitative measurements of the bridge in a 6-year observation span using medium resolution Sentinel-1 SAR images alone but also indirectly identifying potential structural defects in the north section of the bridge. This research is a primer example of how we can deploy diverse evidence to facilitate targeted adaptation measures and prioritize restoration strategies to facilitate asset owners in better decision making.

In this study, an integrated monitoring and evaluation approach was proposed to fix the systematic safeguarding gap of the Great Wall corridor using space technologies. Two representative sections of the Great Wall located in Qingtongxia County and Zhangjiakou City in China were selected for a preliminary comparative investigation to ascertain the coupling mechanism and spatiotemporal characteristics of the driving forces for the heritage damage. The surface deformation rates were estimated by synthetic aperture radar interferometry (InSAR), and the interaction between the deformation rates and the normalized difference vegetation index (NDVI) and meteorological and topographic data was calculated using the correlation matrix. The results showed the following: 1) the surface motions along the observed landscape corridor of the Great Wall were decreased in 2015-2018. 2) The correlation coefficients between the deformation rates and the elevation, slope, annual wind speed, and NDVI were 0.524, 0.115, 0.582, and 0.522, respectively, indicating the dominant influence of surface runoff and high winds on the degradation of the rammed-earth wall in the western arid regions. In contrast, 3) the correlation coefficients between the deformation rates and the aforementioned factors were 0.065, 0.027, 0.025, and 0.052, respectively, indicating negligible effects of natural processes on the degradation of the wall in the eastern section. This study not only provides new insights into preventive monitoring and risk assessment of the entire Great Wall but also highlights the potential of space technologies and a geographical perspective for the sustainable conservation of large-scale heritage sites.

Monitoring deformation of architectural heritage sites is important for the quantitative evaluation of their stability. However, deformation monitoring of sites in mountainous areas remains challenging whether utilizing global navigation satellite system (GNSS) or interferometric synthetic aperture radar (InSAR) techniques. In this study, we improved the small baseline subset (SBAS) approach by introducing the pseudo-baseline combination strategy to avoid the errors caused by inaccurate external DEM, resulting in robust deformation estimations in mountainous areas where the architectural heritage site of the Great Wall is located. First, a simulated dataset and a real dataset were used to verify the reliability and effectiveness of the algorithm, respectively. Subsequently, the algorithm was applied in the landscape deformation monitoring of the Shanhaiguan section of the Great Wall using 51 Sentinel-1 scenes acquired from 2016 to 2018. A thematic stability map of the Shanhaiguan Great Wall corridor was generated, revealing that the landscape was generally stable save for local instabilities due to to unstable rocks and wall monuments. This study demonstrated the capabilities of adaptive multitemporal InSAR (MTInSAR) approaches in the preventive landscape deformation monitoring of large-scale architectural heritage sites in complex terrain.

The Great Wall of China is one of the largest architectural heritage sites globally, and its sustainability is a significant concern. However, its large extent and diverse characteristics cause challenges for deformation monitoring. In this study, the Shanhaiguan section of the Great Wall was investigated in a case study to ascertain the damage and potential hazards of the architectural site. Two standard multi-temporal synthetic aperture radar interferometry (MTInSAR) technologies, including persistent scatterer SAR interferometry (PSInSAR) and small baseline subset (SBAS) SAR interferometry, were used for deformation monitoring using high-resolution TerraSAR-X data acquired in 2015–2017. The results of the two MTInSAR approaches revealed the health condition of the Great Wall. The Shanhaiguan section was stable, but local instabilities caused by rock falls were detected in some mountainous areas. In addition, the applicability of PSInSAR and SBAS was evaluated. The performance analysis of the two approaches indicated that a more reliable and adaptable MTInSAR technique needs to be developed for monitoring the Great Wall. This study demonstrates the potential of MTInSAR technology with high-resolution data for the health diagnosis of heritage sites with a linear structure, such as the Great Wall.