Land subsidence is a global environmental geological hazard caused by natural or human activities. The high spatial resolution and continuous time coverage of interferometric synthetic aperture radar (InSAR) time series analysis techniques provide data and a basis for the development of methods for the investigation and evolution mechanism study of regional land subsidence. Beijing, the capital city of China, has suffered from land subsidence for decades since it was first recorded in the 1950s. It was reported that uneven ground subsidence and fractures have seriously affected the normal operation of the Beijing Capital International Airport (BCIA) in recent years before the overhaul of the middle runway in April 2017. In this study, InSAR time series analysis was successfully used to detect the uneven local subsidence and ground fissure activity that has been gradually increasing in BCIA since 2010. A multi-temporal InSAR (MT-InSAR) technique was performed on 63 TerraSAR-X/TanDem-X (TSX/TDX) images acquired between 2010 and 2017, then deformation rate maps and time series for the airport area were generated. Comparisons of deformation rate and displacement time series from InSAR and ground-leveling were carried out in order to evaluate the accuracy of the InSAR-derived measurements. After an integrated analysis of the distribution characteristics of land subsidence, previous research results, and geological data was carried out, we found and located an active ground fissure. Then main cause of the ground fissures was preliminarily discussed. Finally, it can be conducted that InSAR technology can be used to identify and monitor geological processes, such as land subsidence and ground fissure activities, and can provide a scientific approach to better explore and study the cause and formation mechanism of regional subsidence and ground fissures.

Since the 1970s, land subsidence has been rapidly developing on the Beijing Plain, and the systematic study of the evolutionary mechanism of this subsidence is of great significance in the sustainable development of the regional economy. On the basis of Interferometric Synthetic Aperture Radar (InSAR) results, this study employed the Mann–Kendall method for the first time to determine the mutation information of land subsidence on the Beijing Plain from 2004 to 2015. By combining the hydrogeological conditions, “southern water” project, and other data, we attempted to analyse the reasons for land subsidence mutations. First, on the basis of ENVISAT ASAR and RADARSAT-2 data, the land subsidence of the Beijing Plain was determined while using small baseline interferometry (SBAS-InSAR) and Persistent Scatterers Interferometry (PSI). Second, on the basis of the Geographic Information System (GIS) platform, vector data of displacement under different scales were obtained. Through a series of tests, a scale of 960 metres was selected as the research unit and the displacement rate from 2004 to 2015 was obtained. Finally, a trend analysis of land subsidence was carried out on the basis of the Mann–Kendall mutation test. The results showed that single-year mutations were mainly distributed in the middle and lower parts of the Yongding River alluvial fan and the Chaobai River alluvial fan. Among these mutations, the greatest numbers occurred in 2015 and 2005, being 1344 and 915, respectively. The upper and middle alluvial fan of the Chaobai River, the vicinity of the emergency water sources, and the edge of the groundwater funnel have undergone several mutations. Combining hydrogeological data of the study area and the impact of the south-to-north water transfer project, we analysed the causes of these mutations. The experimental results can quantitatively verify the mutation information of land subsidence in conjunction with time series to further elucidate the spatial-temporal variation characteristics of land subsidence in the study area.

Geological disasters, including ground deformation, fractures and collapse, are serious problems in coal mining regions, which have threatened the sustainable development for local industry. The Ordos Basin is most known for its abundant coal resources. Over-mining the underground coal resources had induced land deformation. Detecting the evolution of the land deformation features and identifying the potential risk are important for decision-makers to prevent geological disasters. We analyzed land subsidence induced by coal mining in a 200 ${\mathrm{km}}^2$ area in the Ordos Basin for the time period 2006–2015. ALOS-1 PALSAR images from December 2006 to January 2011 and ALOS-2 PALSAR-2 images from December 2014 to July 2015, optical remotely sensed images and coal mining information were collected. The small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) method and differential interferometric synthetic aperture radar (D-InSAR) method, GIS and statistical analysis were adopted. Results show that the maximum subsidence rate and cumulative subsidence along the line of sight (LOS) were −65 mm/year and −246 mm, respectively, from December 2006 to January 2011. The maximum cumulative subsidence was −226 mm from December 2014 to July 2015. The new boundary of the mining goafs from 2014 to 2015 and the most dangerous risk region were mapped. Moreover, the effect of large-scale mining coal, with the production volume exceeds 1.2 million tons per year, with the operation time more than 20 years on land subsidence was found greater than small and medium-scale coal mines and reached −59 mm/year. The recently established small-sized and medium-sized coal mines show high land subsidence. This study will contribute to better understand the land subsidence process in mining region and provide scientific support for government to prevent land subsidence.

At present, the research on gesture recognition based on ultrasonic signals is mostly about static gestures. Compared with static gestures, dynamic gestures reflect human intent more intuitively. However, the speed difference of dynamic gestures will cause a mismatch in time series. To tackle this problem, we proposed a dynamic gesture recognition scheme based on the dynamic time warping (DTW) algorithm. First, the ultrasonic signals of the forearm muscles were acquired by four A-mode ultrasonic transducers. Second, the original signals underwent Gaussian filtering, feature extraction, normalization, and principal component analysis (PCA) dimensionality reduction to obtain the main feature information and convert it into time series. Finally, the time series of dynamic gestures were matched based on the DTW algorithm, and the minimum Euclidean distance between different time series was calculated for prediction. We tested five dynamic gestures on ten subjects. The experiments were conducted for a total of ten rounds, and only one round was used as the training set. Results reveal that using the max feature can get an accuracy of 88.0% ± 5.9%. In addition, the energy feature (87.5% ± 6.4%) and the mean feature (85.2% ± 6.9%) also performed well. This research realizes the dynamic gesture recognition of A-mode ultrasonic for the first time, proving the potential of ultrasonic signals, and providing a wider range of use scenarios.

A novel approach was developed to quantitatively examine land subsidence. It combines a new geophysical (NG) prospecting and the interferometric synthetic aperture radar (InSAR) technology to explore uneven development of land subsidence in Beijing, China. We derived land subsidence spatial information over 4 years (from November 2014 to July 2018) based on Sentinel‐1 satellite imagery and the small‐baseline InSAR (SBAS‐InSAR) method. Also, profile data were acquired using seismic frequency resonance (SFR) approach in a few settlement areas. We developed a geological model based on boreholes and SFR data. Thus, we can quantitively study the driving forces of a typical uneven land subsidence. We found that faults are controlling spatial developments of land subsidence in Beijing. The subsidence rates have different values along the same fault. Also, we revealed the contributions of compressible layers to the formation of uneven land subsidence.

In urban areas, deformation of transportation infrastructures may lead to serious safety accidents. Timely and accurate monitoring of the structural deformation is critical for prevention of transportation accidents and assurance of construction quality, particularly in areas with regional land subsidence, such as the city of Beijing. In this study, we proposed a method for the detection of seasonal deformation of highway overpasses using the integration of persistent scatterers Interferometric Synthetic Aperture Radar (PS-InSAR) techniques and seasonal indices, i.e., deformation concentration degree (DCD) and deformation concentration period (DCP) indices. Taking eastern Beijing urban area as a case study area, we first used the PS-InSAR technique to derive time series surface deformation based on 55 TerraSAR-X images during 2010–2016. Then, we proposed DCD and DCP indices to characterize seasonal deformation of 25 highway overpasses in the study area, with DCD representing to what degree the annual deformation is distributed in a year, and DCP representing the period on which deformation concentrates in the year. Our results showed that the maximum annual deformation rate reached −141.3 mm/year in Beijing urban area, and the PS-InSAR measurements agreed well with levelling measurements (R² > 0.97). For PS pixels with DCD ≥ 0.3, the monthly deformation showed obvious seasonal patterns with deformation values during some months greater than those during the other months. DCP revealed that the settlement during autumn and winter was more serious than that in spring and summer. The seasonal patterns seemed to be related to the location, structure, and construction age of the overpasses. The upper-level overpasses, the newly constructed overpasses, and those located in the subsidence area (rate < −40 mm/year) tended to show a greater seasonal pattern. The seasonal deformation variations were also affected by groundwater-level fluctuation, temperature, and compressible layer.