Abstract
As a result of rapid societal development and urbanization, the pumping of groundwater has gradually increased. Land subsidence has thus become a common geological disaster, which can result in huge economic losses. Interferometric synthetic aperture radar (InSAR), with its large-scale and high-accuracy monitoring characteristics, can attain information on Earth surface deformation using the interferometric phase between couples of SAR images acquired at different times. Time-series results for the ground surface are the key information required to understand the deformation pattern and further study the reason for the subsidence. However, in recent research, most methods for resolving time-series deformation—like the Berardino method—that use residuals in functional model solving and distinguish high-pass displacement and the atmospheric component by filtering do not generally work well and functional models focusing on prior information in the time-series solution process are not always available. In this paper, to solve the above problems, 34 Sentinel-1A descending mode scenes of Mexico City captured between 2015/04/13 and 2016/09/10 are used as experimental data. Firstly, a new functional model is provided to obtain the deformation time-series. The nonlinear deformation and atmospheric phase are combined as an unknown parameter and the method of singular value decomposition (SVD) is used to solve this variable. The nonlinear displacement and atmospheric phase are then separated by the singular spectrum analysis (SSA) method. Finally, the total land subsidence time-series is obtained by adding together the linear displacement and nonlinear displacement. Two typical methods and the proposed method were compared using both unit weights and adaptive weights. The experimental results show that the proposed method can obtain a more accurate time-series deformation result. Moreover, the different weights do not result in significant differences and the solved atmospheric and nonlinear phases have good consistency with the interferogram phase.
Highlights
As one of the most important disaster types in regions of dense population, land subsidence is common and can be induced by either natural or human activity
AnTnouadl iVmelionciisthy the effect of the atmosphere and the annual deformation rate could be obtained witTho tdhiemisntaischkitnhge emffeectht oodf .thFeigatumreos4phsehroewans dththeemanenaunalladnedforsmubastiidonenrcaetebceotuwldeebne o2b01ta5i/n0e4d/1w3 itahnd th2e0s1t6a/c0k9in/1g0mgeentheoradt.eFdigwuirteh4thsheo3w4 sStehnetimneela-1n slacenndessu, bwshideernectehebertewfeereennc2e01p5o/i0n4t/h1a3vaenbdee2n01s6e/le0c9t/e1d0in gesntearbalteerdegwioitnhst.he 34 Sentinel-1 scenes, where the reference point have been selected in stable regions
The results of our study indicate that the maximum cumulative deformation exceeds −30 cm in the metropolitan area of Mexico City
Summary
As one of the most important disaster types in regions of dense population, land subsidence is common and can be induced by either natural or human activity. Continuous monitoring of surface deformation is necessary to understand the changing process and minimize damage. Compared with conventional observation technologies, such as leveling and global positioning systems (GPS), interferometric synthetic aperture radar (InSAR) does not need manual intervention and can monitor the surface changes both day and night and in all weather conditions [5,6,7]. InSAR has great advantages in deformation monitoring and has been successfully applied in the monitoring of various kinds of geological disasters, such as volcanic eruptions, earthquakes, landslides and subsidence [10,11,12,13,14,15,16]
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