DInSAR data assimilation for settlement prediction: case study of a railway embankment in the Netherlands

Peduto, Dario; Huber, Maximilian; Speranza, Gianluca; Ruijven, Joris van; Cascini, Leonardo

doi:10.1139/cgj-2016-0425

Cited by 30 publications

(19 citation statements)

References 49 publications

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“…Geological interpretation of satellite data is performed using external data (such as geological, geotechnical, hydrogeological, urbanization and construction process data), by integrating them into a Geographical Information System (GIS). The recognition of the mechanisms is based on cross-comparison of the representative subsoil geological profiles, and the relative displacement time series with multidisciplinary information [14]. Furthermore, the analysis of the breaks and the detection of the deceleration and acceleration periods are fundamental to identify the predisposing and triggering factors.…”

Section: Mechanism Recognitionmentioning

confidence: 99%

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A Methodology to Detect and Characterize Uplift Phenomena in Urban Areas Using Sentinel-1 Data

et al. 2018

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This paper presents a methodology to exploit the Persistent Scatterer Interferometry (PSI) time series acquired by Sentinel-1 sensors for the detection and characterization of uplift phenomena in urban areas. The methodology has been applied to the Tower Hamlets Council area of London (United Kingdom) using Sentinel-1 data covering the period 2015-2017. The test area is a representative high-urbanized site affected by geohazards due to natural processes such as compaction of recent deposits, and also anthropogenic causes due to groundwater management and engineering works. The methodology has allowed the detection and characterization of a 5 km 2 area recording average uplift rates of 7 mm/year and a maximum rate of 18 mm/year in the period May 2015-March 2017. Furthermore, the analysis of the Sentinel-1 time series highlights that starting from August 2016 uplift rates began to decrease. A comparison between the uplift rates and urban developments as well as geological, geotechnical, and hydrogeological factors suggests that the ground displacements occur in a particular geological context and are mainly attributed to the swelling of clayey soils. The detected uplift could be attributed to a transient effect of the groundwater rebound after completion of dewatering works for the recent underground constructions.

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Section: Mechanism Recognitionmentioning

confidence: 99%

“…Several studies report on PSI applications for urban deformation monitoring such as the study of displacement time series of buildings, roads, railways, dams, and tunnels [14][15][16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A Methodology to Detect and Characterize Uplift Phenomena in Urban Areas Using Sentinel-1 Data

et al. 2018

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“…They focused on the implementation of the data assimilation and the assessment of flow properties, using a synthetic case study in which little attention was given to the subsidence models. Peduto et al () used a stochastic technique for the assimilation and prediction of settlement of a railway which did allow quantification of a forecast confidence bound. The latter two examples are implementations of the data assimilation technology that has been applied widely in many disciplines, including groundwater flow and subsurface modelling (Evensen, ), but is not yet very common in interpreting shallow causes of subsidence.…”

Section: Introductionmentioning

confidence: 99%

Disentangling and Parameterizing Shallow Sources of Subsidence: Application to a Reclaimed Coastal Area, Flevoland, the Netherlands

Fokker

Gunnink²,

Koster³

et al. 2019

JGR Earth Surface

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The natural surface area of many coastal and delta plains has been increased by land reclamation in response to growing populations. These reclaimed lands are often experiencing subsidence. The reclaimed South Flevopolder in the coastal plain of the Netherlands has experienced severe subsidence after its reclamation in 1968. The subsidence is caused by phreatic groundwater level lowering and the associated aeration of the former subaqueous shallow subsurface and increased effective stresses. In this study, surface elevation measurements that quantify the subsidence, conducted annually between 1968 and 1993, and in 2009 and 2012, have been used to constrain and estimate the parameters in models that describe subsidence. For the estimation an Ensemble Smoother with Multiple Data Assimilation was employed. For the forward models, we employed correlations for compression (primary consolidation and creep), oxidation, and shrinkage of coastal deposits. Shrinkage of the aerated clay and organic clay layers was found to be the main contributor to subsidence, and the measurements could be represented well. The quantification of the model parameters allows for better subsidence forecasts. The stochastic method that was employed further facilitates to define a quality measure for forecasts in terms of a covariance matrix or a confidence range.

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“…The latter represents nowadays a well-known technique to measure subsidence-induced ground displacements (Peduto et al, 2019) -with an accuracy of a few mm yr −1 (Peduto et al, 2018) -and to monitor structures and infrastructure (Nicodemo et al, 2017;Peduto et al, 2017Peduto et al, , 2018.…”

Section: Introductionmentioning

confidence: 99%

DInSAR data assimilation for optimising maintenance at transition zones between bridges and road embankments: a numerical study

Giangreco¹,

Venmans²,

Peduto³

2020

Proc. IAHS

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Transition zones between bridges and embankments are the most maintenance-prone locations in the road network of The Netherlands due to the very compressible soft soil layers that are widespread in the subsoil of the country. This aspect causes (differential) settlements at the ground surface, damage and maintenance costs for the road owner, and delays to road users. This paper provides the proof-of-concept of an innovative multisource data-driven method based on the assimilation of both conventional settlement plate and satellite DInSAR data in numerical geotechnical modelling with the aim of supporting informed maintenance decisions.

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DInSAR data assimilation for settlement prediction: case study of a railway embankment in the Netherlands

Cited by 30 publications

References 49 publications

A Methodology to Detect and Characterize Uplift Phenomena in Urban Areas Using Sentinel-1 Data

A Methodology to Detect and Characterize Uplift Phenomena in Urban Areas Using Sentinel-1 Data

Disentangling and Parameterizing Shallow Sources of Subsidence: Application to a Reclaimed Coastal Area, Flevoland, the Netherlands

DInSAR data assimilation for optimising maintenance at transition zones between bridges and road embankments: a numerical study

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