In the past few decades, global navigation satellite system (GNSS) technology has been widely used in structural health monitoring (SHM), and the monitoring mode has evolved from long-term deformation monitoring to dynamic monitoring. This paper gives an overview of GNSS-based dynamic monitoring technologies for SHM. The review is classified into three parts, which include GNSS-based dynamic monitoring technologies for SHM, the improvement of GNSS-based dynamic monitoring technologies for SHM, as well as denoising and detrending algorithms. The significance and progress of Real-Time Kinematic (RTK), Precise Point Position (PPP), and direct displacement measurement techniques, as well as single-frequency technology for dynamic monitoring, are summarized, and the comparison of these technologies is given. The improvement of GNSS-based dynamic monitoring technologies for SHM is given from the perspective of multi-GNSS, a high-rate GNSS receiver, and the integration between the GNSS and accelerometer. In addition, the denoising and detrending algorithms for GNSS-based observations for SHM and corresponding applications are summarized. Challenges of low-cost and widely covered GNSS-based technologies for SHM are discussed, and problems are posed for future research.
SUMMARY On 4 and 6 July 2019, an Mw 6.4 foreshock and an Mw 7.1 main shock successively struck the city of Ridgecrest in eastern California. These two events are the most significant earthquake sequences to strike in this part of California for the past 20 yr. We used both continuous global positioning system (GPS) measurements and interferometric synthetic aperture radar (InSAR) images taken by the Sentinel-1 and ALOS-2 satellites in four different viewing geometries to fully map the coseismic surface displacements associated with these two earthquakes. Using these GPS and InSAR measurements both separately and jointly, we inverted data to find the coseismic slip distributions and fault dips caused by the two earthquakes. The GPS-constrained slip models indicate that the Mw 7.1 main shock was predominately controlled by right-lateral motions on a series of northwest-trending faults, while the Mw 6.4 foreshock involved both right-lateral slipping on a northwest-trending fault and left-lateral slipping on a northeast-trending fault. The two earthquakes both generate significant surface slip, with the maximum inferred slip of 5.54 m at the surface. We estimate the cumulative geodetic moment of the two earthquakes to have been 4.93 × 1019 Nm, equivalent to Mw 7.1. Furthermore, our calculations of the changes in static Coulomb stress suggest that the Mw 7.1 main shock was promoted significantly by the Mw 6.4 foreshock. This latest Ridgecrest earthquake sequence ruptured only the northern part of the seismic gap between the 1992 Mw 7.3 Landers earthquake and the 1872 M 7.4–7.9 Owens Valley earthquake. The earthquake risk in this area, therefore, remains very high, considering the significant accumulation of strain in the Eastern California Shear Zone, especially in the southern part of the seismic gap.
The spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) delay-Doppler map (DDM) data collected over ocean carry typical feature information about the ocean surface, which may be covered by open water, mixed water/ice, complete ice, etc. A new method based on Doppler spread analysis is proposed to remotely sense sea ice using the spaceborne GNSS-R data collected over the Northern and Southern Hemispheres. In order to extract useful information from DDM, three delay waveforms are defined and utilized. The delay waveform without Doppler shift is defined as central delay waveform (CDW), while the integration of delay waveforms of 20 different Doppler shift values is defined as integrated delay waveform (IDW). The differential waveform between normalized CDW (NCDW) and normalized IDW (NIDW) is defined as differential delay waveform (DDW), which is a new observable used to describe the difference between NCDW and NIDW, which have different Doppler spread characteristics. The difference is mainly caused by the roughness of reflected surface. First, a new data quality control method is proposed based on the standard deviation and root-mean-square error (RMSE) of the first 48 bins of DDW. Then, several different observables derived from NCDW, NIDW, and DDW are applied to distinguish sea ice from water based on their probability density function. Through validating against sea ice edge data from the Ocean and Sea Ice Satellite Application Facility, the trailing edge waveform summation of DDW achieves the best results, and its probabilities of successful detection are 98.22% and 96.65%, respectively, in the Northern and Southern Hemispheres.
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