Moho Density Research Articles

Moho topographic inversion of the South China Sea based on genetic algorithm

The Parker-Oldenburg method is a commonly used classical interface inversion method for Moho topographic inversion. However, this method is excessively reliant on two hyperparameters − the Moho density contrast and the average Moho depth. Due to the failure to take into account the effect of non-linear terms and computational inefficiencies, this previous method leads to a significant bias in the hyperparameters estimation, which renders it impossible to invert the finer Moho topography. To address this issue, we propose a new method that utilizes the genetic algorithm to estimate more accurate hyperparameters. Synthetic test results illustrate that the differences of the estimated Moho density contrast and average Moho depth from our method and the true values are only 0.044 g/cm3 and 0.729 km, respectively. Compared with the improved Bott’s method, the errors were reduced by 12.28 % and 2.23 %, respectively. To further illustrate the effectiveness of our method, we apply this method to the Southwestern Sub-basin of the South China Sea, where the Moho density contrast and average Moho depth are determined to be 0.61 g/cm3 and 19.18 km, respectively by imposing seismic data constraints. The Moho topography is then inverted based on these determinations, revealing that the Moho topography ranges from 6.3 km to 24.9 km in the study area and exhibits pronounced undulations. Compared to other Moho topography models, our Moho topography is more accurate.

Improved Parker–Oldenburg method and its application to Moho topographic inversion in the northern South China Sea

SUMMARY Before inverting Moho topography, the traditional Parker–Oldenburg method requires the determination of two important hyperparameters, the average Moho depth and Moho density contrast. The selection of these two hyperparameters will directly affect the inversion results. In this paper, a new method for estimating hyperparameters is proposed which is used to improve the Parker–Oldenburg method. The new method is improved by using simulated annealing to accurately estimate the average Moho depth and Moho density contrast based on the relationship between Moho depths and corresponding gravity anomalies at seismic control points. Synthetic tests show that compared to the improved Bott's method and the trial and error method, our method reduces the error in Moho density contrast and average Moho depth by 0.83 and 1.81 per cent, respectively. In addition, compared with the trial and error method, our method greatly improves the computational efficiency. In a practical example, we apply this method to invert the Moho topography in the northern South China Sea. The inversion results show that the Moho topography in the northern South China Sea ranges from 8.2 to 33 km. The root mean squared error between our Moho topography and the seismic validation points is 0.94 km. Compared with the CRUST 1.0 model, our Moho topography is more accurate.

Determining the Moho topography using an improved inversion algorithm: a case study from the South China Sea

The Parker-Oldenburg method, as a classical frequency-domain algorithm, has been widely used in Moho topographic inversion. The method has two indispensable hyperparameters, which are the Moho density contrast and the average Moho depth. Accurate hyperparameters are important prerequisites for inversion of fine Moho topography. However, limited by the nonlinear terms, the hyperparameters estimated by previous methods have obvious deviations. For this reason, this paper proposes a new method to improve the existing Parker-Oldenburg method by taking advantage of the invasive weed optimization algorithm in estimating hyperparameters. The synthetic test results of the new method show that, compared with the trial and error method and the linear regression method, the new method estimates the hyperparameters more accurately, and the computational efficiency performs excellently, which lays the foundation for the inversion of more accurate Moho topography. In practice, the method is applied to the Moho topographic inversion in the South China Sea. With the constraints of available seismic data, the crust-mantle density contrast and the average Moho depth in the South China Sea are determined to be 0.535 g/cm3 and 21.63 km, respectively, and the Moho topography of the South China Sea is inverted based on this. The results of the Moho topography show that the Moho depth in the study area ranges from 5.7 km to 32.3 km, with more obvious undulations. Among them, the shallowest part of the Moho topography is mainly located in the southern part of the Southwestern sub-basin and the southern part of the Manila Trench, with a depth of about 6 km. Compared with the CRUST 1.0 model and the model calculated by the improved Bott’s method, the RMS between the Moho model and the seismic point difference in this paper is smaller, which proves that the method in this paper has some advantages in Moho topographic inversion.

The Accuracy Assessment of Lithospheric Density Models

The Earth’s synthetic gravitational and density models can be used to validate numerical procedures applied for global (or large-scale regional) gravimetric forward and inverse modeling. Since the Earth’s lithospheric structure is better constrained by tomographic surveys than a deep mantle, most existing 3D density models describe only a lithospheric density structure, while 1D density models are typically used to describe a deep mantle density structure below the lithosphere-asthenosphere boundary. The accuracy of currently available lithospheric density models is examined in this study. The error analysis is established to assess the accuracy of modeling the sub-lithospheric mantle geoid while focusing on the largest errors (according to our estimates) that are attributed to lithospheric thickness and lithospheric mantle density uncertainties. Since a forward modeling of the sub-lithospheric mantle geoid also comprises numerical procedures of adding and subtracting gravitational contributions of similar density structures, the error propagation is derived for actual rather than random errors (that are described by the Gauss’ error propagation law). Possible systematic errors then either lessen or sum up after applying particular corrections to a geoidal geometry that are attributed to individual lithospheric density structures (such as sediments) or density interfaces (such as a Moho density contrast). The analysis indicates that errors in modeling of the sub-lithospheric mantle geoid attributed to lithospheric thickness and lithospheric mantle density uncertainties could reach several hundreds of meters, particularly at locations with the largest lithospheric thickness under cratonic formations. This numerical finding is important for the calibration and further development of synthetic density models of which mass equals the Earth’s total mass (excluding the atmosphere). Consequently, the (long-to-medium wavelength) gravitational field generated by a synthetic density model should closely agree with the Earth’s gravitational field.

Moho Geometry Estimation Beneath the Gulf of Guinea From Satellite Gravity Data Based on a Regularized Non‐Linear Gravity Inversion

AbstractIn this work, we present a new gravimetric Moho model over the Gulf of Guinea computed from the inversion of the global gravity EIGEN‐6C4 model. The computation procedure contains a regularized non‐linear inversion algorithm. Before applying the inversion algorithm, the gravity data from the EIGEN‐6C4 model have been corrected from gravimetric effects of topography, bathymetry, and sediments. To calculate these corrections, a forward modeling has been applied using tesseroids to take into account the earth curvature, and via the Legendre Quadrature integration technique. The inversion procedure of the Bouguer disturbances integrates the use of hyperparameters in particular the regularization parameter, the anomalous Moho density contrast and the reference Moho depth. This regularized non‐linear inversion method has been tested on synthetic data contaminated by a normally distributed noise. Through this test, the inversion method showed its effectiveness in estimating a Moho model. The application of this method on the Gulf of Guinea has produced a high‐resolution gravimetric model, whose depths vary between 7.75 and 44.50 km. The greatest depths (greater than 40 km) are located along the eastern part of the study area. The comparison of our gravimetric model with the isostatic Airy, GEMMA (GOCE Exploitation for Moho Modeling and Applications) and CRUST1.0 Moho models, globally shows good similarities. The comparison of our gravimetric Moho model to the GEMMA gravimetric model exhibits large residuals around the continental margin, thus reflecting the limit of the inversion method for areas with great Moho depth gradients. The differences observed between our gravimetric model and the CRUST1.0 model in certain regions such as southern Nigeria show the impact of the neglect of anomalous crustal and mantle sources in the accuracy of the solutions obtained after inversion. However, our gravimetric model yields an improved and high‐resolution representation of the Moho undulations over the Gulf of Guinea compared to previous gravimetric Moho models.

Remaining non-isostatic effects in isostatic-gravimetric Moho determination—is it needed?

SUMMARY For long time the study of the Moho discontinuity (or Moho) has been a crucial topic in inferring the dynamics of the Earth's interior, and with profitable result it is mapped by seismic data, but due to the heterogeneous distribution of such data the quality varies over the world. Nevertheless, with the advent of satellite gravity missions, it is today possible to recover the Moho constituents (i.e. Moho depth; MD and Moho density contrast; MDC) via gravity observations based on isostatic models. Prior to using gravity observations for this application it must be stripped due to the gravitational contributions of known anomalous crustal density structures, mainly density variations of oceans, glacial ice sheets and sediment basins (i.e. stripping gravity corrections). In addition, the gravity signals related mainly with masses below the crust must also be removed. The main purpose of this study is to estimate the significance of removing also remaining non-isostatic effects (RNIEs) on gravity, that is, gravity effects that remain after the stripping corrections. This is carried out by using CRUST19 seismic crustal model and employing Vening Meinesz–Moritz (VMM) gravimetric-isostatic model in recovering the Moho constituents on a global scale to a resolution of 1° × 1°. To reach this goal, we present a new model, named MHUU22, formed by the SGGUGM2 gravitational field, Earth2014 topography, CRUST1.0 and CRUST19 seismic crustal models. Particularly, this study has its main emphasis on the RNIEs on gravity and Moho constituents to find out if we can modify the stripping gravity corrections by a specific correction of the RNIEs. The numerical results illustrate that the RMS differences between MHUU22 MD and the seismic model CRUST1.0 and least-squares combined model MOHV21 are reduced by 33 and 41 per cent by applying the NIEs, and the RMS differences between MHUU22 MDC and the seismic model CRUST1.0 and least-squares combined model MDC21 are reduced by 41 and 23 per cent when the above strategy for removing the RNIEs is applied. Hence, our study demonstrates that the specific correction for the RNIEs on gravity disturbance is significant, resulting in remarkable improvements in MHUU22, which more clearly visualize several crustal structures.

An Improved Method to Moho Depth Recovery From Gravity Disturbance and Its Application in the South China Sea

AbstractTwo hyperparameters, the mean Moho depth and the Moho density contrast, must be specified before the gravity inversion of Moho. Incorrect estimation will impact inversed Moho morphology. The purpose of this study is to present a new gravimetric Moho inversion method. The key improvement of the new method is to accurately estimate the Moho density contrast, based on a linear relationship between the depth of known points and gravity observations. The method is illustrated by a synthetic experiment where the estimated density contrast differs from the true value by only 0.0011 , showing a 93% improvement compared to the initial estimate. The results of processing the noise data show that, our method's accuracy is minimally affected by noise, but is sensitive to the number of known points. Nevertheless, when using only 10 known points, there is still a 50% probability of obtaining a solution with a root mean square (RMS) less than 1 km. When the number of points is greater than 64, the effect of the uniformity of the point distribution is almost negligible. In the real case, we employed the proposed method to invert the South China Sea (SCS) Moho depth. The Moho model reveals that, there is a distinct zoning feature at Moho depth in the SCS, and the 13.5 km isodepth line indicates the continent‐ocean boundary. Furthermore, the RMS of the difference between the gravimetric Moho model and the seismological data is 1.64 km, which is primarily attributed to the lateral variation of the density contrast.

Combination of three global Moho density contrast models by a weighted least-squares procedure

Abstract Due to different structures of the Earth’s crust and mantle, there is a significant density contrast at their boundary, the Moho Density Contrast (or shortly MDC). Frequently one assumes that the MDC is about 600 kg/m3, but seismic and gravimetric data show a considerable variation from region to region, and today there are few such studies, and global models are utterly rare. This research determines a new global model, called MDC21, which is a weighted least-squares combination of three available MDC models, pixel by pixel at a resolution of 1° × 1°. For proper weighting among the models, the study starts by estimating lacking standard errors and (frequently high) correlations among them. The numerical investigation shows that MDC21 varies from 21 to 504 kg/m3 in ocean areas and ranges from 132 to 629 kg/m3 in continental regions. The global average is 335 kg/m3. The standard errors estimated in ocean regions are mostly less than 40 kg/m3, while for continental regions it grows to 80 kg/m3. Most standard errors are small, but they reach to notable values in some specific regions. The estimated MDCs (as well as Moho depths) at mid-ocean ridges are small but show significant variations and qualities.

Geological position of the gold-sulfide-quartz deposits of the Chilean active margin

Research subject. The gold-sulphide-quartz deposits of Central Chile are typical prospecting objects, having small ore intervals (from the first tens of centimetres to 1 m), intermittent and nested ore distribution and extremely uneven gold contents.Materials and methods. The patterns of ore mineralization distribution are considered against the background of the results of modern geophysical studies of the lithosphere: the Moho surface, density and thermal regime of the upper mantle. Detailed studies were conducted on the Yapin ore field.Results. It was shown that the faults controlling gold-sulphidequartz mineralization are derivatives of shear tectonics under the conditions of a transpression regime along the Chilean active margin. At an early stage, these faults developed in a right-shift environment, which was accompanied by the introduction of diabase dikes into the northeastern faults, and gold-sulfide-quartz mineralization superimposed on the dikes was deposited during the left-shift stage. The geological structure of the deposits in the ore field Yapin was characterized. It was shown that a diverse mineralization is developed in the ore field - copper-porphyry, IOCG-type and gold-sulphide-quartz. According to geochemical data, the latter is characterized by a clear enrichment of chalcophilic elements (Au, As, Ag, Cd, Cu, Bi, Pb, Zn, Te, Co). The marked enrichment of Bi, Te and Co ores indicates the participation of magmatic fluid in ore formation and the similarity of the mineralization of the Escondida deposit with the type of gold deposits associated with granitoid intrusions. According to geochemical features, gold-sulphide-quartz mineralization in the general zoning pattern occupies a boundary position between IOCG-type objects and copper-porphyry deposits.Conclusions. The conclusion is drawn about the independence of gold-sulphide-quartz mineralization and its difference from epithermal gold deposits. It is noted that, in the volcanic belts of the North-East of Russia, the prospects for discovering unconventional gold-sulphidequartz deposits similar to those of Central Chile are rather real.

The uncertainty of CRUST1.0

Abstract As crustal structure models based on seismic and other data are frequently used as a-priori information for further geophysical and geological studies and interpretations (e. g., for gravity inversion), it is important to accurately document their qualities. For instance, the uncertainties in published crustal structures deeply affect the accuracies of produced Moho contour maps. The qualities in seismic crustal models arise from several factors such as the survey method, the spatial resolution of the survey (for example the spacing of the shot points and the recording stations), and the analytical techniques utilized to process the data. It is difficult to determine the uncertainties associated with seismic based crustal depth/Moho depth (MD) models, and even more difficult to use such data for estimating the Moho density contrast (MDC) and its accuracy. However, there is another important observable available today, namely global satellite gravitational data, which are fairly homogeneous v. r. t. accuracy and distribution over the planet. For instance, we find by simple error propagation, using the error covariance matrix of the GOCE TIM5 gravitational model, that this model can determine the MD to a global RMS error of 0.8 km with a resolution of about 1° for a known MDC of 200 kg / m 3 \text{kg}/{\text{m}^{3}} . However, the uncertainty in the MDC will further deteriorate the result. We present a new method for estimating the MD and MDC uncertainties of one model by comparing it with another (correlated or uncorrelated) model with known uncertainty. The method is applied in estimating the uncertainty for the CRUST1.0 MD model from four global models (CRUST19, MDN07, GEMMA1.0, KTH15C), yielding mean standard errors varying between 2 and 4.9 km in ocean regions and between 3.2 and 6.0 km on land regions with overall means of 3.8±0.4 and 4.8 ± 0.6 km 4.8\pm 0.6\hspace{0.1667em}\text{km} , respectively. Also, starting from the KTH15C MDC model, the mean standard error of CRUST1.0 MDC was estimated to 47.4 and 48.3 kg / m 3 \text{kg}/{\text{m}^{3}} for ocean and land regions, respectively.

Moho density contrast in Antarctica determined by satellite gravity and seismic models

SUMMARY As recovering the crust–mantle/Moho density contrast (MDC) significantly depends on the properties of the Earth's crust and upper mantle, varying from place to place, it is an oversimplification to define a constant standard value for it. It is especially challenging in Antarctica, where almost all the bedrock is covered with a thick layer of ice, and seismic data cannot provide a sufficient spatial resolution for geological and geophysical applications. As an alternative, we determine the MDC in Antarctica and its surrounding seas with a resolution of 1° × 1° by the Vening Meinesz-Moritz gravimetric-isostatic technique using the XGM2019e Earth Gravitational Model and Earth2014 topographic/bathymetric information along with CRUST1.0 and CRUST19 seismic crustal models. The numerical results show that our model, named HVMDC20, varies from 81 kg m−3 in the Pacific Antarctic mid-oceanic ridge to 579 kg m−3 in the Gamburtsev Mountain Range in the central continent with a general average of 403 kg m−3. To assess our computations, we compare our estimates with those of some other gravimetric as well as seismic models (KTH11, GEMMA12C, KTH15C and CRUST1.0), illustrating that our estimates agree fairly well with KTH15C and CRUST1.0 but rather poor with the other models. In addition, we compare the geological signatures with HVMDC20, showing how the main geological structures contribute to the MDC. Finally, we study the remaining glacial isostatic adjustment effect on gravity to figure out how much it affects the MDC recovery, yielding a correlation of the optimum spectral window (7≤ n ≤12) between XGM2019e and W12a GIA models of the order of ∼0.6 contributing within a negligible $ \pm 14$ kg m−3 to the MDC.

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

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.

Unique geological structures of the Law Dome and Vanderford and Totten glaciers region (Wilkes Land) distinguished by geophysical data

Wilkes Land is a key region for Gondwana reconstruction, however it remains one of the largest regions on Earth with poorest knowledge of geology. This study comprehensively reviews the ICECAP/ IceBridge geophysical data for the Law Dome region including Vanderford and Totten adjacent glaciers over Wilkes Land and their role in obtaining new insight on the East Antarctic geology hidden under the ice cover. We analyzed more than 100,000 line kilometers of new magnetic, gravity and subglacial bedrock topography data that are available through the National Snow and Ice Data Center (USA). The newly acquired data supports our previous idea of the continuous rift structure existence at the southern boundary of Law Dome that runs between Vanderford and Totten Glaciers. The rift length exceeds 400 km and width varies from 50 to 100 km. In accordance with results of depth to Moho estimations and density modelling, for axial part of the rift it is characteristic an essential thinning of the Earth crust thickness, it is raised up to 24–26 km and continue to be elevated along entire length of this structure. The thickness of sedimentary rocks within the rift exceeds 3 km, their high density probably evidence that they were formed during Late Paleozoic – Early Mesozoic. The results of our investigations support tectonic nature of this structure as continuous rift developed since the Mesozoic extension phase (~160 Ma) of the Wilkes Land continental margin. Second distinctive structure is the strong reversely magnetized Law Dome magnetic anomaly with an area of about 9,500 km2. This anomaly would map out one of the largest mafic/ultramafic intrusions of the Earth, similar in extent to Norway’s Bjerkreim-Sokndal layered intrusion, the Coompana Block gabbro in Australia, or even the granitic-gneiss complex in the Adirondack Mountains of North America.

Recovering Moho constituents from satellite altimetry and gravimetric data for Europe and surroundings

Abstract In this research, we present a local Moho model, named MOHV19, including Moho depth and Moho density contrast (or shortly Moho constituents) with corresponding uncertainties, which are mapped from altimetric and gravimetric data (DSNSC08) in addition to seismic tomographic (CRUST1.0) and Earth topographic data (Earth2014) to a resolution of 1° × 1° based on a solution of Vening Meinesz-Moritz’ theory of isostasy. The MOHV19 model covers the area of entire European plate along with the surrounding oceans, bounded by latitudes (30 °N–82 °N) and longitudes (40 °W–70 °E). The article aims to interpret the Moho model resulted via altimetric and gravimetric information from the geological and geophysical perspectives along with investigating the relation between the Moho depth and Moho density contrast. Our numerical results show that estimated Moho depths range from 7.5 to 57.9 km with continental and oceanic averages of 41.3 ± 4.9 km and 21.6 ± 9.2 km, respectively, and an overall average of 30.9 ± 12.3 km. The estimated Moho density contrast ranges from 60.2 to 565.8 kg/m3, with averages of 421.8 ± 57.9 and 284.4 ± 62.9 kg/m3 for continental and oceanic regions, respectively, with a total average of 350.3 ± 91.5 kg/m3. In most areas, estimated uncertainties in the Moho constituents are less than 3 km and 40 kg/m3, respectively, but they reach to much more significant values under Iceland, parts of Gulf of Bothnia and along the Kvitoya Island. Comparing the Moho depths estimated by MOHV19 and those derived by CRUST1.0, MDN07, GRAD09 and MD19 models shows that MOHV19 agree fairly well with CRUST1.0 but rather poor with other models. The RMS difference between the Moho density contrasts estimated by MOHV19 and CRUST1.0 models is 49.45 kg/m3.

Contribution of satellite altimetry in modelling Moho density contrast in oceanic areas

Abstract 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.

Towards the Moho depth and Moho density contrast along with their uncertainties from seismic and satellite gravity observations

Abstract We present a combined method for estimating a new global Moho model named KTH15C, containing Moho depth and Moho density contrast (or shortly Moho parameters), from a combination of global models of gravity (GOCO05S), topography (DTM2006) and seismic information (CRUST1.0 and MDN07) to a resolution of 1° × 1° based on a solution of Vening Meinesz-Moritz’ inverse problem of isostasy. This paper also aims modelling of the observation standard errors propagated from the Vening Meinesz-Moritz and CRUST1.0 models in estimating the uncertainty of the final Moho model. The numerical results yield Moho depths ranging from 6.5 to 70.3 km, and the estimated Moho density contrasts ranging from 21 to 650 kg/m3, respectively. Moreover, test computations display that in most areas estimated uncertainties in the parameters are less than 3 km and 50 kg/m3, respectively, but they reach to more significant values under Gulf of Mexico, Chile, Eastern Mediterranean, Timor sea and parts of polar regions. Comparing the Moho depths estimated by KTH15C and those derived by KTH11C, GEMMA2012C, CRUST1.0, KTH14C, CRUST14 and GEMMA1.0 models shows that KTH15C agree fairly well with CRUST1.0 but rather poor with other models. The Moho density contrasts estimated by KTH15C and those of the KTH11C, KTH14C and VMM model agree to 112, 31 and 61 kg/m3 in RMS. The regional numerical studies show that the RMS differences between KTH15C and Moho depths from seismic information yields fits of 2 to 4 km in South and North America, Africa, Europe, Asia, Australia and Antarctica, respectively.

Determination of crustal thickness under Tibet from gravity-gradient data

We develop a novel regional inversion algorithm to determinate the Moho geometry from the gravity-gradient data. The procedure involves two steps. Firstly, the refined gravity-gradient data, which comprise mainly the gravitational signal of the Moho geometry, are obtained from applying the gravimetric forward modeling by using a tesseroid method. Secondly, the refined gravity-gradient data are used to find the Moho depth. The functional relationship between the (known) refined gravity-gradient data and the (unknown and sought) Moho depth is defined by means of the (nonlinear) Fredholm integral equation of the first kind, which is further linearized by means of applying a Taylor series. To stabilize the inverse solution of the system of observation equations, the Tikhonov regularization is applied. The developed algorithm is tested at the study area of Tibet characterized by the largest crustal thickness. Results of the gravimetric inversion (for the uniform and variable Moho density contrast models) have a relatively good agreement with the seismic model (CRUST1.0) at the level of expected uncertainties of about 5km without the presence of a significant systematic bias.

Moho modeling in spatial domain: A case study under Tibet

We develop and apply the algorithm for a regional gravimetric Moho recovery in spatial domain. The functional relation between the (known) refined gravity field and the (unknown and sought) Moho depth is defined by means of solving the Fredholm integral equation of the first kind. Linearization is applied to define the Moho depth corrections with respect to the mean Moho depth. The system of linearized observation equations is solved to find the Moho depth corrections, and Tikhonov’s regularization is applied to stabilize this ill-posed inverse problem. Developed algorithm is applied to model the Moho depth regionally under the Tibetan Plateau and Himalayas, while adopting the uniform and variable Moho density contrast models, and gravimetric results are validated using the CRUST1.0 seismic model. Our results show a relatively good agreement between gravimetric and seismic models, without presence of a significant systematic bias. Our result, however, indicates that for a regional study the variable Moho density contrast might not improve the results especially when density structure of the lower crust and uppermost mantle is not know accurately. In that case, the use of the uniform Moho density contrast for a regional Moho recovery is more appropriate.

Isostatic GOCE Moho model for Iran

One of the major issues associated with a regional Moho recovery from the gravity or gravity-gradient data is the optimal choice of the mean compensation depth (i.e., the mean Moho depth) for a certain area of study, typically for orogens characterised by large Moho depth variations. In case of selecting a small value of the mean compensation depth, the pattern of deep Moho structure might not be reproduced realistically. Moreover, the definition of the mean compensation depth in existing isostatic models affects only low-degrees of the Moho spectrum. To overcome this problem, in this study we reformulate the Sjöberg and Jeffrey’s methods of solving the Vening-Meinesz isostatic problem so that the mean compensation depth contributes to the whole Moho spectrum. Both solutions are then defined for the vertical gravity gradient, allowing estimating the Moho depth from the GOCE satellite gravity-gradiometry data. Moreover, gravimetric solutions provide realistic results only when a priori information on the crust and upper mantle structure is known (usually from seismic surveys) with a relatively good accuracy. To investigate this aspect, we formulate our gravimetric solutions for a variable Moho density contrast to account for variable density of the uppermost mantle below the Moho interface, while taking into consideration also density variations within the sediments and consolidated crust down to the Moho interface. The developed theoretical models are applied to estimate the Moho depth from GOCE data at the regional study area of the Iranian tectonic block, including also parts of surrounding tectonic features. Our results indicate that the regional Moho depth differences between Sjöberg and Jeffrey’s solutions, reaching up to about 3km, are caused by a smoothing effect of Sjöberg’s method. The validation of our results further shows a relatively good agreement with regional seismic studies over most of the continental crust, but large discrepancies are detected under the Oman Sea and the Makran subduction zone. We explain these discrepancies by a low quality of seismic data offshore.

Moho Density Contrast in Central Eurasia from GOCE Gravity Gradients

Seismic data are primarily used in studies of the Earth’s inner structure. Since large parts of the world are not yet sufficiently covered by seismic surveys, products from the Earth’s satellite observation systems have more often been used for this purpose in recent years. In this study we use the gravity-gradient data derived from the Gravity field and steady-state Ocean Circulation Explorer (GOCE), the elevation data from the Shuttle Radar Topography Mission (SRTM) and other global datasets to determine the Moho density contrast at the study area which comprises most of the Eurasian plate (including parts of surrounding continental and oceanic tectonic plates). A regional Moho recovery is realized by solving the Vening Meinesz-Moritz’s (VMM) inverse problem of isostasy and a seismic crustal model is applied to constrain the gravimetric solution. Our results reveal that the Moho density contrast reaches minima along the mid-oceanic rift zones and maxima under the continental crust. This spatial pattern closely agrees with that seen in the CRUST1.0 seismic crustal model as well as in the KTH1.0 gravimetric-seismic Moho model. However, these results differ considerably from some previously published gravimetric studies. In particular, we demonstrate that there is no significant spatial correlation between the Moho density contrast and Moho deepening under major orogens of Himalaya and Tibet. In fact, the Moho density contrast under most of the continental crustal structure is typically much more uniform.