Expressions for the Global Gravimetric Moho Modeling in Spectral Domain

Tenzer, Róbert; Chen, Wenjin

doi:10.1007/s00024-013-0740-4

Cited by 18 publications

(8 citation statements)

References 46 publications

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“…Gravimetric determination of crustal thickness comprises, in principle, two numerical procedures (TENZER and CHEN 2014a). Gravimetric forward modeling is first used to compute the gravity data (i.e., the consolidated crust-stripped gravity disturbances), which are highly spatially correlated with the Moho geometry (cf.…”

Section: Theoretical Modelmentioning

confidence: 99%

“…The Moho geometry was determined in accordance with the procedure described by TENZER and CHEN (2014a). The inverse solution was performed iteratively by use of a Gauss-Seidel scheme (YOUNG 1971).…”

Section: Moho Geometrymentioning

confidence: 99%

“…In this study, we propose an alternative method, which incorporates the variable Moho density contrast as the a-priori information in gravimetric determination of the Moho depth. For this purpose, we reformulate the expressions for the gravimetric forward and inverse modeling given by TENZER and CHEN (2014a). Their method assumed a uniform model of the Moho density contrast.…”

Section: Introductionmentioning

confidence: 99%

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Effect of Upper Mantle Density Structure on Moho Geometry

Tenzer

Chen

Jin

2014

Pure Appl. Geophys.

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A constant value of the Moho density contrast is often assumed in the gravimetric methods used for determination of Moho geometry. This assumption might be sufficient in regional studies with a relatively homogenous lithospheric structure (and, consequently, small lateral variations in Moho density contrast). In global studies, however, this assumption is not reasonable, because not only the Moho depth but also the Moho density contrast vary substantially, and are, thus, likely to result in systematic errors in Moho geometry determined globally from gravity data. In this study we address this issue by investigating the effect of variable Moho density contrast on Moho geometry. We demonstrate that assumption of variable Moho density contrast (instead of a uniform model) substantially improves agreement between the global gravimetric and seismic Moho models by approximately 30 %.

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Section: Theoretical Modelmentioning

confidence: 99%

Section: Moho Geometrymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effect of Upper Mantle Density Structure on Moho Geometry

Tenzer

Chen

Jin

2014

Pure Appl. Geophys.

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“…Then, Bagherbandi (2012) compared three gravity inversion methods for crustal thickness modeling and applied them in Tibet, giving a conclusion that Sjöberg's direct solution was better than the Paker-Oldenburg's algorithm [27,33] and the iterative method developed from the VMM model [34]. Further, Tenzer (2014) solved VMM in spectral domain [35], whose efficiency was improved by Chen (2017) using the Fast Fourier Transform (FFT) technique [36]. Eshagh et al (2016aEshagh et al ( , 2016bEshagh et al ( , 2017 [37][38][39] showed an approach to Moho modeling from the vertical gravity gradients (Γ zz ) of the on-orbit GOCE data, whose precision was better than that inverted by the global gravity field model.…”

Section: Introductionmentioning

confidence: 99%

An Approach to Moho Topography Recovery Using the On-Orbit GOCE Gravity Gradients and Its Applications in Tibet

Wan

Luo

et al. 2019

Remote Sensing

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It is significant to determine the refined Moho topography for understanding the tectonic structure of the crust and upper mantle. A novel method to invert the Moho topography from the on-orbit gravity gradients is proposed in the present study. The Moho topography of Tibet is estimated by our method, which is verified by previous studies. The research results show that: (1) the deepest Moho of Tibet, approximately 70 km, is located at the western Kunlun area, where it corresponds well to that of previous publications; (2) clear Moho folds can be observed from the inverted Moho topography, whose direction presents a clockwise pattern and is in good agreement with that of Global Positioning System; (3) compared with the CRUST 1.0, our inverted Moho model has a better spatial resolution and reveals more details for tectonic structure; (4) the poor density model of the crust in Tibet may be the main reason for the differences between the obtained gravity Moho model and seismic Moho model; (5) by comparing our inverted Moho with those from previous publications, our method is correct and effective. This work provides a new method for the study of Moho topography and the interior structure of the Earth.

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“…Bai et al (2014) mapped a model of crustal thickness using marine gravity data corrected for the sediment thickness, thermal expansion, and pressure compression in the South China Sea and showed the influence of these factors on gravity-derived estimates of the MD. Tenzer and Chen (2014) applied a new method to estimate the MD using the gravimetric forward and inverse modeling in the spectral domain, and Hamayun (2014) produced the MD using gravity disturbance together with two seismic models. Tenzer et al (2015a) used the CRUST1.0 model to estimate the average densities of crustal structures, and compile the gravity field quantities derived by the Earth's crustal structures and investigate their spatial and spectral characteristics and their correlation with the crustal geometry in context of the gravimetric Moho determination.…”

Section: Introductionmentioning

confidence: 99%

Estimating a combined Moho model for marine areas via satellite altimetric - gravity and seismic crustal models

Majid

Sjöberg

2019

Stud Geophys Geod

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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 altimetryderived 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.

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Expressions for the Global Gravimetric Moho Modeling in Spectral Domain

Cited by 18 publications

References 46 publications

Effect of Upper Mantle Density Structure on Moho Geometry

Effect of Upper Mantle Density Structure on Moho Geometry

An Approach to Moho Topography Recovery Using the On-Orbit GOCE Gravity Gradients and Its Applications in Tibet

Estimating a combined Moho model for marine areas via satellite altimetric - gravity and seismic crustal models

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