Existing studies have shown that satellite synthetic aperture radar (SAR) interferometry has two apparent drawbacks, i.e., temporal decorrelation and atmospheric contamination, in the application of deformation mapping. It is however possible to improve deformation analysis by tracking some natural or man-made objects with steady radar reflectivity, i.e., permanent scatterers (PS), in the frame of time series of SAR images acquired over the same area. For detecting land subsidence in Shanghai, China, this paper presents an attempt to explore an approach of PS-neighborhood networking SAR interferometry. With use of 26 ERS-1/2 SAR images acquired 1992 through 2002 over Shanghai, the analysis of subsiding process in time and space is performed on the basis of a strong network which is formed by connecting neighboring PSs according to a distance threshold. The linear and nonlinear subsidence, atmospheric effects as well as topographic errors can be separated effectively in this way. The subsidence velocity field in 10 years over Shanghai is also derived. It was found that the annual subsidence rates in the study area range from -2.1 to -0.6 cm/yr, and the averaged subsidence rate reaches -1.1 cm/yr.

The 2016 moment magnitude (Mw) 7.8 Kaikoura earthquake demonstrated that multiple fault segments can undergo rupture during a single seismic event. Here, we employ Global Positioning System (GPS) observations and geodetic modeling methods to create detailed images of coseismic slip and postseismic afterslip associated with the Kaikoura earthquake. Our optimal geodetic coseismic model suggests that rupture not only occurred on shallow crustal faults but also to some extent at the Hikurangi subduction interface. The GPS-inverted moment release during the earthquake is equivalent to a Mw 7.9 event. The near-field postseismic deformation is mainly derived from right-lateral strike-slip motions on shallow crustal faults. The afterslip did not only significantly extend northeastward on the Needles fault but also appeared at the plate interface, slowly releasing energy over the past 6 months, equivalent to a Mw 7.3 earthquake. Coulomb stress changes induced by coseismic deformation exhibit complex patterns and diversity at different depths, undoubtedly reflecting multi-fault rupture complexity associated with the earthquake. The Coulomb stress can reach several MPa during coseismic deformation, which can explain the trigger mechanisms of afterslip in two high-slip regions and the majority of aftershocks. Based on the deformation characteristics of the Kaikoura earthquake, interseismic plate coverage, and historical earthquakes, we conclude that Wellington is under higher seismic threat after the earthquake and great attention should be paid to potential large earthquake disasters in the near future.

As one of the critical factors affecting the accuracy of inertial navigation system (INS) positioning, random noise will lead to the accumulation of INS positioning errors, even the failure of the system. Previous studies mainly focus on achieving noise reduction by adopting either the wavelet decomposition method or some relevant methods of the empirical mode decomposition (EMD). However, the former is limited by Heisenberg’s uncertainty principle when processing non-stationary signals, and the latter exhibits numerous mode-mixing phenomena during the decomposition process. In this paper, a permutation fuzzy entropy (PFE) based improved complete ensemble EMD with adaptive noise (ICEEMDAN) de-noising method is proposed to utilize the ICEEMDAN method for signal decomposition, and the PFE as an index to distinguish the effective intrinsic mode functions (IMFs) and the noisy IMFs during the decomposition process. In addition, simulated signals with different noise levels and field data with different durations are utilized to design a group of experiments to test the de-noising performance of the PFE-based ICEEMDAN de-noising method. The experimental results demonstrate that the advantages of our proposed method consists of three main parts: (1) The ICEEMDAN method more effectively alleviates the mode-mixing problem compared to the complete ensemble EMD with adaptive noise (CEEMDAN), thereby enhancing the accuracy of inertial measurement unit (IMU) signal reconstruction; (2) the PFE is more preferable than the permutation entropy for classify IMFs decomposed by the ICEEMDAN method; (3) our proposed method can effectively reduce IMU signal noise and improve the positioning accuracy of inertial sensor.

The integrated navigation system frequently degrades to an inertial navigation system (INS) standalone in complex urban environments due to recurring signal interruptions of the Global Navigation Satellite System (GNSS). Nonholonomic constraints (NHCs) and odometry assistance can effectively suppress the accumulation of INS errors during GNSS outages. However, traditional NHC requires precise compensation for inertial measuring unit (IMU) installation angles, while odometry is inaccessible in many applications. To address these challenges, we propose a novel velocity hybrid regression network (VHRNet), which integrates the superiority of convolutional neural network (CNN) and Transformer for IMU data mining. This network establishes a mapping between IMU outputs and virtual NHC and odometry measurement to apply 3-D velocity full constraints without IMU installation angles compensation and hardware-wheeled odometry. Moreover, a low-complexity adaptive optimization model in the variance domain is introduced, which can adaptively adjust noise covariance to mitigate the impact of gross errors and frequent stopping events. Land vehicle experiments demonstrate that VHRNet effectively improves the prediction accuracy of 3-D velocities, achieving 0.028, 0.562, and 0.034 m/s in lateral, forward, and vertical directions, respectively. Compared to classic time series prediction models, the prediction accuracy for the 3-D velocity improves by more than 12.5%, 21.7%, and 17.0%. In various GNSS outage scenarios, the positioning performance of the pseudo-3-D velocity constraint method significantly surpasses the traditional method with precise installation angle compensation. Notably, when GNSS outages for 120 s, positioning accuracy improves by 87.3% compared to single INS. In addition, the implementation of a noise-adaptive model yields more precise and robust positioning results.