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  • 1.
    Abrehdary, Majid
    et al.
    University West, Department of Engineering Science, Division of Mathematics, Computer and Surveying Engineering.
    Sjöberg, Lars Erik
    Hogskolan i Gavle, Gavle, Sweden .
    Estimating a combined Moho model for marine areas via satellite altimetric - gravity and seismic crustal models2019In: Studia Geophysica et Geodaetica, ISSN 0039-3169, E-ISSN 1573-1626, p. 1-25Article in journal (Refereed)
    Abstract [en]

    Isostasy is a key concept in geoscience in interpreting the state of mass balance between the Earth's lithosphere and viscous asthenosphere. A more satisfactory test of isostasy is to determine the depth to and density contrast between crust and mantle at the Moho discontinuity (Moho). Generally, the Moho can be mapped by seismic information, but the limited coverage of such data over large portions of the world (in particular at seas) and economic considerations make a combined gravimetric-seismic method a more realistic approach. The determination of a high-resolution of the Moho constituents for marine areas requires the combination of gravimetric and seismic data to diminish substantially the seismic data gaps. In this study, we estimate the Moho constituents globally for ocean regions to a resolution of 1° × 1° by applying the Vening Meinesz-Moritz method from gravimetric data and combine it with estimates derived from seismic data in a new model named COMHV19. The data files of GMG14 satellite altimetry-derived marine gravity field, the Earth2014 Earth topographic/bathymetric model, CRUST1.0 and CRUST19 crustal seismic models are used in a least-squares procedure. The numerical computations show that the Moho depths range from 7.3 km (in Kolbeinsey Ridge) to 52.6 km (in the Gulf of Bothnia) with a global average of 16.4 km and standard deviation of the order of 7.5 km. Estimated Moho density contrasts vary between 20 kg m-3 (north of Iceland) to 570 kg m-3 (in Baltic Sea), with a global average of 313.7 kg m-3 and standard deviation of the order of 77.4 kg m-3. When comparing the computed Moho depths with current knowledge of crustal structure, they are generally found to be in good agreement with other crustal models. However, in certain regions, such as oceanic spreading ridges and hot spots, we generally obtain thinner crust than proposed by other models, which is likely the result of improvements in the new model. We also see evidence for thickening of oceanic crust with increasing age. Hence, the new combined Moho model is able to image rather reliable information in most of the oceanic areas, in particular in ocean ridges, which are important features in ocean basins.

  • 2.
    Eshagh, Mehdi
    et al.
    University West, Department of Engineering Science, Division of Computer, Electrical and Surveying Engineering. Royal Institute of Technology, Division of Geodesy, Stockholm, Sweden .
    Lars E:, Sjöberg
    Royal Institute of Technology, Division of Geodesy, Stockholm, Sweden .
    The modified best quadratic unbiased non-negative estimator (MBQUNE) of variance components2008In: Studia Geophysica et Geodaetica, ISSN 0039-3169, E-ISSN 1573-1626, Vol. 52, no 3, p. 305-320Article in journal (Refereed)
    Abstract [en]

    Estimated variance components may come out as negative numbers without physical meaning. One way out of this problem is to use non-negative methods. Different approaches have been presented for the solution. Sjöberg presented a method of Best Quadratic Unbiased Non-Negative Estimator (BQUNE) in the Gauss-Helmert model. This estimator does not exist in the general case. Here we present the Modified BQUNE (MBQUNE) obtained by a simple transformation from the misclosures used in the BQUE to residuals. In the Gauss-Markov adjustment model the BQUNE and MBQUNE are identical, and they differ in condition and Gauss-Helmert models only by a simple transformation. If the observations are composed of independent/disjunctive groups the MBQUNE exists in any adjustment model and it carries all the properties of the BQUNE (when it exists). The presented variance component models are tested numerically in some simple examples. It is shown that the MBQUNE works well for disjunctive groups of observations.

  • 3.
    Eshagh, Mehdi
    et al.
    Department of Geodesy, K.N.Toosi University of Technology, Tehran, Iran.
    Romeshkani, Mohsen
    Department of Geodesy, K.N.Toosi University of Technology, Tehran, Iran.
    Quality assessment of terrestrial gravity anomalies from GOCE gradiometric data and Earth's gravity models using variance component estimation2013In: Studia Geophysica et Geodaetica, ISSN 0039-3169, E-ISSN 1573-1626, Vol. 57, no 1, p. 67-83Article in journal (Refereed)
    Abstract [en]

     The satellite gravity gradiometry (SGG) data of the recent European satellite mission, the Gravity field and steady-state Ocean Circulation Explorer (GOCE), can be used as an external source for quality description of terrestrial gravity anomalies and the Earth's gravity models (EGMs). In this study integral estimators are provided and modified in a least-squares sense to regenerate the SGG data of GOCE from terrestrial gravity anomalies and an existing EGM. Based on the differences between the generated and real GOCE SGG data, condition adjustment models are constructed and variance component estimation (VCE) is applied for balancing the a priori errors of data with these differences. Here, a 1-month orbit of GOCE is considered over Iran and the condition adjustment models and VCE process are used to calibrate the errors of the GOCE data, terrestrial gravity anomalies of the area and the EGM. Numerical studies over Iran show that the a priori errors of the GOCE data and the EGM were properly presented. Also the average error of the terrestrial gravity anomalies, with a resolution of 0.5° × 0.5°, after condition adjustment and VCE process using Tzz, Tx, Tyz and −Txx −Tyy is about 30 mGal.

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