An overview of ground surface displacements generated by groundwater dynamics, revealed by InSAR techniques

Radutu, Alina; Nedelcu, Ion; Gogu, Constantin Radu

doi:10.1016/j.proeng.2017.11.137

Cited by 12 publications

(7 citation statements)

References 9 publications

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“…This extensive use of underground water resources and increase in the pumping stations due to increase in the population is causing significant loss in small and large aquifers in most parts of the world, creating problems related to drainage system, flooding during rainy seasons, and the functioning of structures such as canals [6][7][8]. Numerous events of localized land subsidence have been reported in the past couple of decades in various parts of the world, especially due to extensive groundwater discharge [9]. Variation in seasonal timings and change in precipitation patterns have disturbed the runoff and recharge in aquifer systems.…”

Section: Introductionmentioning

confidence: 99%

Ground Deformation and Its Causes in Abbottabad City, Pakistan from Sentinel-1A Data and MT-InSAR

Shahzad

Ding

et al. 2020

Remote Sensing

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Land subsidence, as one of the engineering geological problems in the world, is generally caused by compression of unconsolidated strata due to natural or anthropogenic activities. We employed interferometric point target analysis (IPTA) as a multi-temporal interferometric synthetic aperture radar (MT-InSAR) technique on ascending and descending Sentinel-1A the terrain observation with progressive scans SAR (TOPSAR) images acquired between January 2015 and December 2018 to analyze the spatio-temporal distribution and cause of subsidence in Abbottabad City of Pakistan. The line of sight (LOS) average deformation velocities along ascending and descending orbits were decomposed into vertical velocity fields and compared with geological data, ground water pumping schemes, and precipitation data. The decomposed and averaged vertical velocity results showed significant subsidence in most of the urban areas in the city. The most severe subsidence was observed close to old Karakorum highway, where the subsidence rate varied up to −6.5 cm/year. The subsidence bowl profiles along W–E and S–N transects showed a relationship with the locations of some water pumping stations. The monitored LOS time series histories along an ascending orbit showed a close correlation with the rainfall during the investigation period. Comparative analysis of this uneven prominent subsidence with geological and precipitation data reflected that the subsidence in the Abbottabad city was mainly related to anthropogenic activities, overexploitation of water, and consolidation of soil layer. The study represents the first ever evidence of land subsidence and its causes in the region that will support the local government as well as decision and policy makers for better planning to overcome problems of overflowing drains, sewage system, littered roads/streets, and sinking land in the city.

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Section: Introductionmentioning

confidence: 99%

Ground Deformation and Its Causes in Abbottabad City, Pakistan from Sentinel-1A Data and MT-InSAR

Shahzad

Ding

et al. 2020

Remote Sensing

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“…The amplitude represents target reflectance, while the phase represents surface variations. A SAR interferogram is formed by combining the phase of two SAR pictures obtained concurrently or at different times from satellite with slightly varying look angles [1,3,10,13,6]. The phase difference of two pictures, called the master image and slave image, collected on the same region from two orbits separated by a given baseline distance is used in radar interferometry [2].…”

Section: Introductionmentioning

confidence: 99%

“…The InSAR data is useful in studying ocean currents, landscape studies, vegetation characteristics, terrain categorization, glacial motions, and landslide surveillance. It can be also used to study the ground surface displacement in the urban, rural or hilly regions [10]. Multiple SAR images can be combined to generate a radar interferogram.…”

Section: Introductionmentioning

confidence: 99%

“…Multiple SAR images can be combined to generate a radar interferogram. The interference pattern induced by the phase shift between these pictures can be used to quantify topography or small changes in terrain of order of a few millimetres along the spacecraft look path [1,3,10,13,6,2,14]. The changes in the returned phase can be accurately calculated using the phase difference between two radar picture obtained with the same point of look but at separate times.…”

Section: Introductionmentioning

confidence: 99%

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InSAR Data Analysis using Deep Neural Networks

Agrawal

Verma

Pasari

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Among all natural disasters, earthquakes cause the most severe devastation to both infrastructure as well as human lives. The pattern of surface deformation from satellite-based measures, such as interferometric synthetic aperture radar (InSAR), helps understand crustal dynamics and associated seismic hazards in tectonically active areas. In the last few years, the exponentially rising volume of InSAR data has led to the formulation of automatic crustal deformation algorithms through data-intensive machine learning models. In this study, we propose the long-short-term memory (LSTM) architecture of deep neural networks to forecast InSAR-based line-of-sight displacement. The model suitably captures the inherent temporal variations of SAR interferograms. The method implementation on a region near Central and East Java, Indonesia shows satisfactory performance (mean absolute percentage error around 2%) of the proposed LSTM model comprising four layers, each consisting of 100 nodes. We therefore conclude that deep neural network is a promising avenue for InSAR time series analysis.

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“…One of these is the groundwater flow when considering its interaction with the urban environment [5,8,9]. In many cases, mostly because of the groundwater pumping, the effect triggering land subsidence can be observed at the ground surface [1,[10][11][12][13][14][15][16]. On an extensive scale, the vertical displacements of the ground surface integrate different hydrological, hydrogeological, geological, geotechnical phenomena, as well as other anthropogenic interventions [9,[17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Sentinel-1 Data for Underground Processes Recognition in Bucharest City, Romania

et al. 2020

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Urban areas are strongly influenced by the different processes affecting the underground and implicitly the terrestrial surface. Land subsidence can be one of the effects of the urban processes. The identification of the vulnerable areas of the city, prone to subsidence, can be of great help for a sustainable urban planning. Using Sentinel-1 data, by the PSI (persistent scatterer interferometry) technique, a vertical displacements map of Bucharest city has been prepared. It covers the time interval 2014–2018. Based on this map, several subsidence areas have been identified. One of them, holding a thick layer of debris from urban constructions, was analyzed in detail, on the basis of an accurate local geological model and by correlating the local displacements with the urban groundwater system hydraulic heads. The properties of the anthropogenic layer have been characterized by complementary geotechnical and hydrogeological studies. A dynamic instability pattern, highlighted by PSI results, has been put into evidence when related to this type of anthropogenic layer. This thick anthropogenic layer and its connections to the urban aquifer system have to be further analyzed, when the procedures of urban planning and design invoke constructive operations modifying the aquifer dynamics.

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An overview of ground surface displacements generated by groundwater dynamics, revealed by InSAR techniques

Cited by 12 publications

References 9 publications

Ground Deformation and Its Causes in Abbottabad City, Pakistan from Sentinel-1A Data and MT-InSAR

Ground Deformation and Its Causes in Abbottabad City, Pakistan from Sentinel-1A Data and MT-InSAR

InSAR Data Analysis using Deep Neural Networks

Sentinel-1 Data for Underground Processes Recognition in Bucharest City, Romania

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