Sequential InSAR Time Series Deformation Monitoring of Land Subsidence and Rebound in Xi’an, China

Wang, Baohang; Zhao, Chaoying; Zhang, Qin; Peng, Mimi

doi:10.3390/rs11232854

Cited by 25 publications

(24 citation statements)

References 27 publications

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“…9. This is distinct from a rebound area in Xi'an introduced in [40]. Instead, IC1 mapped the spatial range of rebound deformation in Yuhuazhai, well south and west of Xi'an.…”

Section: B Spatiotemporal Deformation Pattern Analysismentioning

confidence: 89%

“…Large-scale InSAR data have been used to investigate the spatiotemporal characteristics of subsidence and ground fissures in Xi'an. These include global positioning system and InSAR observations from 2005 to 2006 [44]: Envisat, ALOS, and Ter-raSAR SAR datasets from 2005 to 2012 [45]; ERS, Envisat, and Sentinel SAR datasets from 2003 to 2017 [46]; ALOS, TerraSAR, and Sentinel SAR datasets from 2012 to 2018 [47]; and Sentinel SAR datasets from 2015 to 2019 [40]. These studies showed that degradation of the aquifer system had led to complex subsidence and ground fissures.…”

Section: A Study Areamentioning

confidence: 99%

“…Previous research has mapped ground deformation characteristics using different sensors and across different time spans [40], [44]- [47]. With our newly developed continuously updating multisensor InSAR deformation time-series approach, we were able to map the evolution of long-term surface deformation in Xi'an from January 3, 2007 to February 12, 2021, which is well suited to large-scale, near-real-time monitoring.…”

Section: B Spatiotemporal Deformation Pattern Analysismentioning

confidence: 99%

See 2 more Smart Citations

Long-Term Continuously Updated Deformation Time Series From Multisensor InSAR in Xi'an, China From 2007 to 2021

Wang

Zhao

Zhang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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According to historical records, land subsidence has been occurring in Xi'an, China, since the 1960s, characterized by complex land subsidence patterns. This subsidence has the potential to cause serious societal and economic problems during the process of urbanization. Long-term, large-scale monitoring and dynamic high precision tracking of the evolution of surface deformation associated with geohazards is a prerequisite for effective prevention or advance warning of geological disasters. Synthetic aperture radar interferometry (InSAR), a satellite remote sensing technology, can facilitate such monitoring. Both currently operating and planned SAR satellites would provide extensive amounts of SAR data. In this article, we describe an approach for continuously updating long-term multisensor InSAR deformation time series using robust sequential least squares. It is successfully applied to near-real-time monitoring of the long-term evolution of surface deformation in Xi'an, China, from January 3, 2007 to February 12, 2021, using four SAR satellites: ALOS/PALSAR-1, TerraSAR-X, ALOS/PALSAR-2, and Sentinel-1A. In order to analyze deformation evolution, temporal independent component analysis was used to interpret deformation patterns. We found that land subsidence in Xi'an has slowed and even halted in some areas. However, large areas are uplifting, which presents a potential for geohazards. We conclude that the proposed approach using continuously updated deformation time series from multisensor InSAR can provide nearreal-time deformation measurements, which are necessary for an early warning system.

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“…9. This is distinct from a rebound area in Xi'an introduced in [40]. Instead, IC1 mapped the spatial range of rebound deformation in Yuhuazhai, well south and west of Xi'an.…”

Section: B Spatiotemporal Deformation Pattern Analysismentioning

confidence: 89%

Section: A Study Areamentioning

confidence: 99%

Section: B Spatiotemporal Deformation Pattern Analysismentioning

confidence: 99%

See 1 more Smart Citation

Long-Term Continuously Updated Deformation Time Series From Multisensor InSAR in Xi'an, China From 2007 to 2021

Wang

Zhao

Zhang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…The subway line usually passes through the central area with large population and dense buildings. The excavation of subway tunnel will also change the original equilibrium of the stratum (including rock-soil layer and aquifer), resulting in varied degrees of ground subsidence [4], [5]. The subsidence causes two kinds of safety problems.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Subsidence Deformation of Suzhou Subway Using InSAR Timeseries Analysis

Zhao

et al. 2021

IEEE Access

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“…On the basis of orthogonal triaxial-stress experiments with CH 4 seepage, a complete stress-strain relationship and the corresponding evolution of volumetric strain and permeability were obtained. Wang et al [14] investigated the effect of pore-fluid pressure on normal deformation by laboratory experiments. Experiment results indicated that pore-fluid pressure significantly affects the normal deformation of a jointed sample.…”

Section: Introductionmentioning

confidence: 99%

An Improved New Convolutional Neural Network Method for Inverting the Pore Pressure in Oil Reservoir by Surface Vertical Deformation

Wang

et al. 2021

Lithosphere

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Average pore pressure in oil formation is an important parameter to measure energy in the formation and the capacity of injection–production. In past studies, average pore pressure mainly depends on pressure build-up test results, which have a high cost and need a long testing time, so it is not conducive to utilize. In this paper, the vertical deformation of the Earth’s surface was used to calculate changes in reservoir pore pressure. We set up the marker stake for measuring ground displacement and measured the vertical deformation at the in situ position. Furthermore, we provided an improved new convolutional neural network (CNN), which adopted image-to-image mode, removed pooling layers and full connection layers, and used a new loss function considering the boundary influence coefficient matrix. Then, the machine learning method was used to invert surface vertical deformation to change pore pressure in the oil reservoir. The method was tested in the block of the Sazhong X development zone, in the Daqing Oilfield of China. The average pore-pressure change values of 20 grids whose area was 900×900 m each were determined by the inversion of the corresponding surface vertical deformation at 27 marker stakes. The pore pressure change accuracy reached 82.34%. This study provides a new method for calculating average pore pressure in terms of an oil block. At the same time, it also provides a new technical method for inversion calculations.

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Sequential InSAR Time Series Deformation Monitoring of Land Subsidence and Rebound in Xi’an, China

Abstract: Interferometric synthetic aperture radar (

Cited by 25 publications

References 27 publications

Long-Term Continuously Updated Deformation Time Series From Multisensor InSAR in Xi'an, China From 2007 to 2021

Long-Term Continuously Updated Deformation Time Series From Multisensor InSAR in Xi'an, China From 2007 to 2021

Monitoring Subsidence Deformation of Suzhou Subway Using InSAR Timeseries Analysis

An Improved New Convolutional Neural Network Method for Inverting the Pore Pressure in Oil Reservoir by Surface Vertical Deformation

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