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  • 1.
    Abrehdary, Majid
    et al.
    University West, Department of Engineering Science, Division of Mathematics, Computer and Surveying Engineering.
    Sjöberg, Lars E.
    University West, Department of Engineering Science, Division of Mathematics, Computer and Surveying Engineering. Royal Institute of Technology (KTH), Division of Geodesy and Satellite Positioning, Stockholm, SE-100 44, Sweden.
    Sampietro, D.
    GReD S.r.l., Via Cavour 2, Lomazzo (CO), 22074, Italy.
    Contribution of satellite altimetry in modelling Moho density contrast in oceanic areas2018In: Journal of Applied Geodesy, ISSN 1862-9016, E-ISSN 1862-9024Article in journal (Refereed)
    Abstract [en]

    The determination of the oceanic Moho (or crust-mantle) density contrast derived from seismic acquisitions suffers from severe lack of data in large parts of the oceans, where have not yet been sufficiently covered by such data. In order to overcome this limitation, gravitational field models obtained by means of satellite altimetry missions can be proficiently exploited, as they provide global uniform information with a sufficient accuracy and resolution for such a task. In this article, we estimate a new Moho density contrast model named MDC2018, using the marine gravity field from satellite altimetry in combination with a seismic-based crustal model and Earth's topographic/bathymetric data. The solution is based on the theory leading to Vening Meinesz-Moritz's isostatic model. The study results in a high-accuracy Moho density contrast model with a resolution of 1° × 1° in oceanic areas. The numerical investigations show that the estimated density contrast ranges from 14.2 to 599.7 kg/m3 with a global average of 293 kg/m3. In order to evaluate the accuracy of the MDC2018 model, the result was compared with some published global models, revealing that our altimetric model is able to image rather reliable information in most of the oceanic areas. However, the differences between this model and the published results are most notable along the coastal and polar zones, which are most likely due to that the quality and coverage of the satellite altimetry data are worsened in these regions.

  • 2.
    Alizadeh-Khameneh, Mohammad Amin
    et al.
    KTH Royal Institute of Technology, Division of Geodesy and Satellite Positioning, Stockholm, Sweden.
    Eshagh, Mehdi
    University West, Department of Engineering Science, Division of Mathematics, Computer and Surveying Engineering.
    Jensen, Anna O.
    KTH Royal Institute of Technology, Division of Geodesy and Satellite Positioning, Stockholm, Sweden.
    Optimization of deformation monitoring networks using finite element strain analysis2018In: Journal of Applied Geodesy, ISSN 1862-9016, E-ISSN 1862-9024, Vol. 2, no 2, p. 187-197Article in journal (Refereed)
    Abstract [en]

    An optimal design of a geodetic network can fulfill the requested precision and reliability of the network, and decrease the expenses of its execution by removing unnecessary observations. The role of an optimal design is highlighted in deformation monitoring network due to the repeatability of these networks. The core design problem is how to define precision and reliability criteria. This paper proposes a solution, where the precision criterion is defined based on the precision of deformation parameters, i. e. precision of strain and differential rotations. A strain analysis can be performed to obtain some information about the possible deformation of a deformable object. In this study, we split an area into a number of three-dimensional finite elements with the help of the Delaunay triangulation and performed the strain analysis on each element. According to the obtained precision of deformation parameters in each element, the precision criterion of displacement detection at each network point is then determined. The developed criterion is implemented to optimize the observations from the Global Positioning System (GPS) in Skåne monitoring network in Sweden. The network was established in 1989 and straddled the Tornquist zone, which is one of the most active faults in southern Sweden. The numerical results show that 17 out of all 21 possible GPS baseline observations are sufficient to detect minimum 3 mm displacement at each network point. © 2018 Walter de Gruyter GmbH, Berlin/Boston.

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