A combined global Moho model based on seismic and gravimetric data

Eshagh, Mehdi; Bagherbandi, Mohammad; Sjöberg, Lars E.

doi:10.1556/ageod.46.2011.1.3

Cited by 25 publications

(6 citation statements)

References 16 publications

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“…Since proper spherical model equations can be quite easily derived and linearized [see e.g. Eshagh et al 2011], global applications of the collocation method seem to be feasible.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, satellite dedicated gravity missions [Reigber et al 1999, Albertella et al 2002, Tapley et al 2004] have made available a large amount of data that can be profitably used in combination with ground based gravity data [Shin et al 2007]. Recently, an approach based on the integration of gravity and seismic information has been proposed by Eshagh et al [2011]. They devised a method based on a stochastic combination of seismic and gravity Moho models.…”

Section: Introductionmentioning

confidence: 99%

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The collocation approach to Moho estimate

Barzaghi

Biagi

2014

Annals of Geophysics

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<p>In this paper, the collocation approach to Moho estimate is presented. This method is applied to the inversion of gravity data that can be complemented by Moho depth information coming from e.g. seismic information. In this context, a two layers model is considered and discussed in order to give a general theoretical framework for the inversion method. A body with two inner constant density layers and an inner separation surface between is considered and a uniqueness theorem is proved for the estimability of the separation surface given the gravity outside the body itself. Based on this result, a discussion is given on the estimation of the Moho depths based on terrestrial gravity observations. The observation equation is presented and its local planar approximation is derived. The application of the collocation method to the estimate of Moho depths is then studied and discussed in relationship to the planar observation equation. Also, numerical tests are presented. To this aim, the collocation inversion algorithm is implemented and tested on simulated data to prove its effectiveness. The results show that the proposed method is reliable provided that proper data reductions for model discrepancies are taken into account.</p>

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“…Since proper spherical model equations can be quite easily derived and linearized [see e.g. Eshagh et al 2011], global applications of the collocation method seem to be feasible.…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The collocation approach to Moho estimate

Barzaghi

Biagi

2014

Annals of Geophysics

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“…Moreover, the advent of satellite dedicated gravity missions (Reigber et al, 1999;Tapley et al, 2004;Floberghagen et al, 2011), made possible to estimate the Moho depth at global scale. Eshagh et al (2011) and Reguzzoni et al (2013) proposed methods that optimally combine seismic and gravimetric data for Moho estimate.…”

Section: Introductionmentioning

confidence: 99%

A recovered Moho model by integrated inversion of gravity and seismic depths in Iran

Ebadi

Safari

Barzaghi

et al. 2020

Heliyon

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This research aims to define the depth of Moho in Iran by collocation method using gravimetric data with seismic information. The definition of the Moho in the Iranian region is of considerable importance due to the geological complexity of the area also characterized by tectonic and orogenic events of particular uniqueness. We applied the collocation method to Moho recovery using the gravity data generated by GOCO03S model reduced by topography/bathymetry, sediment and consolidated crust effects from CRUST1.0. These data have been complemented with seismic Moho depth estimates. A compilation of 213-points seismic depth has been collected over Iran and used in the integrated gravimetric-seismic inversion. Among them, 140 seismic points have been selected completely random and included as data in the integrated collocation approach for Moho depth estimation. The 73 remaining seismic points have been used as checking points for validating the estimated Moho. In the first run, gravity data only have been considered to collocation Moho recovery. When comparing this gravimetric solution with the 73 seismic checking points, a standard deviation of 6.2 km was found. In case of considering the regional seismic depths into the collocation approach, the standard deviation of the residuals between our results and seismic checking Moho depths improved to 4.9 km. It must be stated that, even in the integrated inversion, a significant discrepancy between the seismic and the integrated gravimetric-seismic Moho is present in the South Caspian Basin. Low quality of CRUST1.0 could explain this inconsistency in this area.

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“…This approach was later proceeded by some additional theoretical studies for recoverying also the Moho density contrast (MDC) (Sjöberg and Bagherbandi, 2011) and improved for isolating the Bouguer gravity anomaly from non-isostatic effects (Bagherbandi and Sjöberg, 2012). Eshagh et al (2011) presented a Moho model from a combination of the EGM2008 and seismic model based on the VMM method. Wang et al (2011) released gravity-derived crustal thickness models for the North Atlantic Ocean and the Chain Fracture Zone, which are calibrated using seismically determined crustal thickness, and he concluded that about 7% of the ocean crust is less than 4 km thick, 58% is 47 km thick, and the remaining 35% is more than 7 km thick.…”

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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A combined global Moho model based on seismic and gravimetric data

Cited by 25 publications

References 16 publications

The collocation approach to Moho estimate

The collocation approach to Moho estimate

A recovered Moho model by integrated inversion of gravity and seismic depths in Iran

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

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