Gravity Anomalies Research Articles

Two-Step Clustering for Mineral Prospectivity Mapping: A Case Study from the Northeastern Edge of the Jiaolai Basin, China

The advancement of geological big data has rendered data-driven methodologies increasingly vital in Mineral Prospectivity Mapping. The effective integration of quantitative and qualitative data, including experiential and knowledge-based insights, is crucial in geological data fusion. Specifically, the conversion of raw data into samples and the selection of predictive methods are two core issues that constitute the focus of this study. Traditional clustering methods require the user to specify the number of clusters in advance. The two-step clustering can automatically determine the clustering result ‘k’ while analyzing both continuous and categorical variables, by building a Cluster Feature (CF) and using information criteria to merge nodes. In this study, we conducted an analysis utilizing stream sediment element data, residual gravity anomalies, and fault distribution through the two-step clustering method. Factor analysis (FA) was employed to reduce 16 elemental variables from stream sediments into five uncorrelated continuous variables; additionally, residual gravity anomalies were transformed from continuous to categorical variables via an interval-based method before being combined with fault distribution, resulting in seven variables for clustering. The research findings indicate that categorical variables significantly influence clustering results; concurrently, as the importance of continuous variables within the cluster increases, so does k. When only one categorical variable is present, residual gravity anomalies show significantly better clustering than fault distribution; however, when two categorical variables are involved, it is essential to consider the quantity of categories: more categories lead to poorer quality. The results from the Jiaolai Basin’s northeastern margin indicate a significant correlation with known gold deposits; two-step clustering is a promising and effective method for improving mineral prospecting efforts.

Integrating Multimodal Deep Learning with Multipoint Statistics for 3D Crustal Modeling: A Case Study of the South China Sea

Constructing an accurate three-dimensional (3D) geological model is crucial for advancing our understanding of subsurface structures and their evolution, particularly in complex regions such as the South China Sea (SCS). This study introduces a novel approach that integrates multimodal deep learning with multipoint statistics (MPS) to develop a high-resolution 3D crustal P-wave velocity structure model of the SCS. Our method addresses the limitations of traditional algorithms in capturing non-stationary geological features and effectively incorporates heterogeneous data from multiple geophysical sources, including 44 wide-angle seismic crustal structure profiles obtained by ocean bottom seismometers (OBSs), gravity anomalies, magnetic anomalies, and topographic data. The proposed model is rigorously validated against existing methods such as Kriging interpolation and MPS alone, demonstrating superior performance in reconstructing both global and local spatial features of the crustal structure. The integration of diverse datasets significantly enhances the model’s accuracy, reducing errors and improving the alignment with known geological information. The resulting 3D model provides a detailed and reliable representation of the SCS crust, offering critical insights for studies on tectonic evolution, resource exploration, and geodynamic processes. This work highlights the potential of combining deep learning with geostatistical methods for geological modeling, providing a robust framework for future applications in geosciences. The flexibility of our approach also suggests its applicability to other regions and geological attributes, paving the way for more comprehensive and data-driven investigations of Earth’s subsurface.

Impacts of Digital Elevation Model Elevation Error on Terrain Gravity Field Calculations: A Case Study in the Wudalianchi Airborne Gravity Gradiometer Test Site, China

Accurate Digital Elevation Models (DEMs) are essential for precise terrain gravity field calculations, which are critical in gravity field modeling, airborne gravimeter and gradiometer calibration, and geophysical inversion. This study evaluates the accuracy of various satellite DEMs by comparing them with a LiDAR DEM at the Wudalianchi test site, a location requiring ultra-accurate terrain gravity fields. Major DEM error sources, particularly those related to vegetation, were identified and corrected using a least squares method that integrates canopy height, vegetation cover, NDVI, and airborne LiDAR DEM data. The impact of DEM vegetation errors on terrain gravity anomalies and gravity gradients was quantified using a partitioned adaptive gravity forward-modeling method at different measurement heights. The results indicate that the TanDEM-X DEM and AW3D30 DEM exhibit the highest vertical accuracy among the satellite DEMs evaluated in the Wudalianchi area. Vegetation significantly affects DEM accuracy, with vegetation-related errors causing an impact of approximately 0.17 mGal (RMS) on surface gravity anomalies. This effect is more pronounced in densely vegetated and volcanic regions. At 100 m above the surface and at an altitude of 1 km, vegetation height affects gravity anomalies by approximately 0.12 mGal and 0.07 mGal, respectively. Additionally, vegetation height impacts the vertical gravity gradient at 100 m above the surface by approximately 4.20 E (RMS), with errors up to 48.84 E over vegetation covered areas. The findings underscore the critical importance of using DEMs with vegetation errors removed for high-precision terrain gravity and gravity gradient modeling, particularly in applications such as airborne gravimeter and gradiometer calibration.

Evaluation of GOCE/GRACE and combined global geopotential models using GNSS/levelling data over Nigeria

AbstractGlobal Geopotential Models (GGMs) provide valuable information about Earth’s gravity field functionals, such as geoid heights and gravity anomalies. However, ground-based datasets are required to validate these GGMs at the regional and local scales. In this study, we validated the accuracy of GGMs by comparing them with ground-based Global Navigation Satellite System (GNSS)/levelling data for the first time in Nigeria. We employed two validation scenarios: with and without considering spectral consistency using the spectral enhancement method (SEM) to incorporate high and very high frequencies of the gravity field spectrum from the combined global gravity field model (XGM2019e_2159) and the residual terrain model (RTM) derived from the Shuttle Radar Topography Mission (SRTM) data, respectively. The results of this evaluation confirmed that the application of SEM improved the assessment of the GGM solutions in an unbiased manner. Integrating XGM2019e_2159 and SRTM data to constrain the high-frequency component of geoid heights in Gravity Field and Steady-State Ocean Circulation Explorer (GOCE)-based GGMs led to an improvement of approximately 10% in reducing the standard deviation (STD) relative to when SEM was not applied. GO_CONS_GCF_2_TIM_R6 at spherical harmonics (SH) of up to degree and order 260 demonstrated the lowest STD when compared to GO_CONS_GCF_2_DIR_R6 and GO_CONS_GCF_2_SPW_R5, with a reduction from 0.380 m without SEM application to 0.342 m with SEM implementation. In addition, four transformation models, namely, linear, four-parameter, five-parameter, and seven-parameter models, were evaluated. The objective is to mitigate the reference system offsets between the GNSS/levelling data and the GGMs and to identify the particular parametric model with the smallest STD across all GGMs. This effort reduced the GGMs misfits to GNSS/levelling to 0.30 m, representing a 15.3% decrease in STD. Notably, the XGM2019e_2159 model provides this improvement.

Gravity and magnetic study of the Xitle volcanic field, Basin of Mexico – Inferences on the pre-eruptive topography

Results of a gravity and magnetic study over a lava field in the southern Basin of Mexico are used to investigate on lava emplacement and pre-eruptive topography. The characteristics of lava flows depend on multiple factors, including the lava composition, effusion rate, rheology, fluid content and underlying terrain. The ∼2000 years old volcanic field, formed by seven flow units emplaced by the Xitle eruption, covers an extensive area >80 km2 characterized by topographic relief on the Ajusco volcano flank and surrounding plain. The study is conducted on the northern end of the volcanic field. The gravity and magnetic study is based on regional-residual separation, vertical and horizontal derivatives, reduced to the pole, analytical signal, spectral analysis and forward modeling. The regional gravity field shows two anomaly lows in the central sector, with the residual field showing trends of gravity highs. The residual magnetic anomaly is characterized by trends of magnetic highs associated to the lava field. Depths estimated from spectral analysis of gravity and magnetic anomalies show shallow sources at ∼22 m and ∼46 m, and deep sources at ∼469 m and ∼268 m, respectively. The underground sequence is modeled as a volcanoclastic succession of varying thickness. The first vertical derivatives, gradients and analytical signal enhance the anomalies associated to lava thickness variations. Joint gravity and magnetic analysis constraints the deep/shallow anomaly sources in the volcanic field and relations to pre-eruptive structure.

Analysis of the Influence of Different Reference Models on Recovering Gravity Anomalies from Satellite Altimetry

Analysis of the Influence of Different Reference Models on Recovering Gravity Anomalies from Satellite Altimetry

At the border of peri-Gondwana and Baltica – the structure of the eastern termination of the Variscan belt

The termination of the eastern Variscan belt has been a matter of long-standing debates since it is deeply concealed beneath younger sediments. Its geometry has been addressed by two competing hypotheses of the Variscan orocline and right-lateral strike-slip tectonics. Therefore, a set of new gravity and magnetic maps was compiled for Czechia, Poland and eastern Germany to track Variscan structures in the subsurface. These maps clearly show that the Rheno-Hercynian Suture turns almost 90° east the of the Harz Mts. and continues into Poland toward the ESE. Therefore, our study favours a concept of semi-orocline that terminates in the east against the Brunovistulian Block. The pattern of magnetic and gravity anomalies combined with geological evidence indicates two stages of accretion superimposed in the eastern Variscan belt. The initial W-E convergence led to the formation of NNE-SSW-trending structures that dominate the southern Bohemian Massif. These structures were overprinted by an important N-S shortening event that shaped WNW-ESE oriented features. The latter run parallel to the Baltica margin and dominate the area NE of the Elbe Fault. This is consistent with seismic data showing large-scale underthrusting of the Baltica crust beneath the Variscan belt at a distance of at least 100 km.

Deep Forest Modeling: An Interpretable Deep Learning Method for Mineral Prospectivity Mapping

AbstractAccurate mineral prediction is crucial for reducing costs and uncertainties in mineral discovery and extraction. The use of artificial intelligence and big data has advanced mineral prediction into intelligent forecasting. Machine learning methods have shown significant promise in enhancing outcomes. Currently, neural network‐based approaches dominate deep learning (DL), but they lack interpretability and have high modeling complexity, making them less effective for complex problems and time‐consuming. Deep Forest, an innovative DL paradigm, addresses these issues by dynamically adjusting complexity and providing importance assessments for predictive factors. This study focuses on the North American Cordillera, known for its rich geological data and potential for porphyry copper deposits (PCDs). Predictions are made using Deep Forest with factors like Euclidean distance between faults and magmatic rock, fault line density, gravity anomalies, and stream‐sediment geochemical data. Deep neural networks, random forest, convolutional neural networks, transformer model and graph convolutional networks are also used for comparison. Deep Forest shows high performance and can avoid the black box problem of DL without relying on other tools in DL, providing a new perspective for the development and application of other non‐neural network DL models for mineral prediction. Feature importance analysis shows that geological structure and magmatism significantly influence PCD prediction. Elevated levels of elements like Al, Co, and Cr in stream sediments help identify mineralization‐related alterations. These findings underscore Deep Forest's capability to accurately and efficiently guide mineral exploration, highlighting its potential as a promising approach for mineral prospecting.

Lunar Low‐Titanium Magmatism During Ancient Expansion Inferred From Ejecta Originating From Linear Gravity Anomalies

AbstractLinear gravity anomalies (LGAs) on the Moon have been interpreted as ancient magmatic intrusions formed during the lunar expansion. The composition of such ancient subsurface intrusions may offer hints for the lunar thermodynamic state in the initial stage of lunar history. To pose a first compositional constraint on magmatism related to lunar expansion, this study analyzed the spectrum and gravity around craters on LGAs, such as Rowland, Roche, and Edison craters. Using reflectance spectra around the craters, we first surveyed non‐mare basaltic exposures. To test the LGA excavation scenario as a possible origin of the discovered exposures, we then compared the Gravity Recovery and Interior Laboratory data and post‐cratering gravity simulation with the iSALE shock physics code. Our spectral analysis reveals no basaltic exposure around the Rowland crater. Further, the observed termination of LGA at the crater rim contradicts the gravity simulation, which assumes that LGA predates the Rowland crater. These results suggest that LGA formation might postdate the Rowland formation and that lunar expansion lasted even after the Nectarian age. On the other hand, we found that both Roche and Edison craters possess basaltic exposures in their peripheries. Because the gravity reduction inside Roche crater can be reproduced in our simulation, the discovered basaltic exposures are possibly LGA materials ejected from these craters. The composition of those exposures shows that the LGA intrusions at the two locations are composed of low‐titanium magma, indicating that ancient magma during the expansion did not contain ilmenite‐rich melt, perhaps resulting from the low‐ilmenite content of the ancient upper mantle.

Строение центральной части палеопротерозойского Лосевского террейна по данным плотностного моделирования

The results of detailed three-dimensional density modeling of the upper crust of the central part of the Losevsky terrane located in the northern part of the Voronezh crystal massif are considered. The distribution of absolute crustal density values of to a depth of 16 km was obtained as a result of the inversion of local anomalies of the gravitational field in the Bouguer reduction. The solution of the inverse problem of gravimetry was implemented within the framework of the initial model, which was formed using digital geological and geophysical data from the study area: the regional density model of the East European Platform lithosphere and its corresponding regional gravity field; the field of local gravity anomalies, representing the difference between the observed and regional fields of the lithosphere model; generalized information on the density of sedimentary cover rocks and the crystal basement; the thickness scheme of the «gravity-active» layer, obtained on the basis of statistical analysis of the anomalous field; geological and topographic information. Quantitative interpretation of the gravimetry data allowed us to obtain fundamentally new information about the geological structure of the upper crust of the study area. The reliability of the obtained results is confirmed by the consistency of a priori data and the correspondence of the total model (local plus regional) and observed gravitational fields.

Inversion of Gravity Anomalies by a Hybrid Metaheuristic Algorithm

In this work, we introduce application of a hybrid algorithm (DE/PSO) to estimate the model parameters from residual gravity anomalies due to some simple geometrical bodies. This algorithm combines differential evolution (DE) and particle swarm optimization (PSO). To investigate the performance of the hybrid algorithm, test studies were carried out using synthetic and field data sets. The synthetic data sets include noise-free and noisy synthetic anomalies. Two published gravity anomalies from Cuba and Canada were used as the field data. In the hybrid algorithm, DE and PSO yield [premature] solutions separately and share their best solutions during an iterative process. An openly accessible metaheuristics package (NMOF) in R programming environment was used to implement the hybrid algorithm. Through simulations using synthetic anomalies, DE/PSO algorithm was successful to provide improved results. In comparison to the solutions from the single algorithms (DE and PSO), the DE/PSO algorithm shows more effectiveness in terms of accuracy and convergence. The true model parameters of noise-free and noisy synthetic gravity anomalies were recovered well by the hybrid algorithm. The results of inversion for the field examples are characterized by low residual values between the observed gravity anomalies and the calculated ones.

Detecting edges of geologic sources from gravity or magnetic anomalies through a novel algorithm based on hyperbolic tangent function

Detecting edges of geologic sources from gravity or magnetic anomalies through a novel algorithm based on hyperbolic tangent function

Performance assessment of sentinel-3/6 altimeter data for marine gravity recovery

High-precision sea surface height is crucial for determining the marine gravity field. The Sentinel-3/6 altimetry missions, equipped with SRAL and Poseidon-4 altimeters, provide this essential data. However, there is a lack of comprehensive assessment of the Sentinel-3/6 altimeters for inverting marine gravity anomalies (MGA). In this study, we employ the inverse Venning-Meinsz method to derive nine sets of 1’×1’ MGAs in the South China Sea (SCS) and the Ross Sea (RS). Specifically, MGAs from Sentinel-3A, Sentinel-3B, Sentinel-6 SARM, Sentinel-6 LRM, HY-2A, ICESat-2, and CryoSat-2 are denoted as S3A, S3B, S6S, S6L, H2A, IS2, and CS2, respectively. MGA from the combined HY-2A, ICESat-2, and CryoSat-2 is referred to as HIC, while 3SHIC denotes the MGA from the combination of Sentinel-3/6 SARM, HY2A, ICESat-2, and CryoSat-2. We assess the performance of these MGAs using the EGM08, DTU17, SIO V32.1, and SDUST2021 gravity field models, as well as shipboard gravity across different ocean regions. Among the Sentinel-3/6 MGAs, S3B exhibits the highest accuracy in the SCS, with a root mean square error (RMSE) of 5.277 mGal, followed closely by S3A. Conversely, S3A demonstrates the highest accuracy with an RMSE of 4.635 mGal, followed by S3B in the RS. The inversion accuracy of MGAs from S6S and S6L are comparable, though S6S outperforms S6L in the open sea. The performance of MGAs from Sentinel-3/6 matches or surpasses that of other altimetry missions during the same period. In the SCS, the best-performing MGA is 3SHIC, with an RMSE of 4.585 mGal, closely matching DTU17. However, 3SHIC exhibits superior performance in the RS with an RMSE of 4.263 mGal compared to DTU17 and SDUST 2021. Furthermore, the performance of 3SHIC, which integrates Sentinel-3/6 data, improves that of HIC by 0.74% and 3.37% in the SCS and RS, respectively. These results underscore the contribution of Sentinel-3/6 altimeters to the MGA, particularly in coastal and high-latitude regions. Integration of Sentinel-3/6 data with other altimetry satellites is expected to enhance the spatial resolution and accuracy of the global marine gravity field, especially with the successful establishment of the network of Sentinel-6 in the future.

High-Efficiency Forward Modeling of Gravitational Fields in Spherical Harmonic Domain with Application to Lunar Topography Correction

Gravity forward modeling as a basic tool has been widely used for topography correction and 3D density inversion. The source region is usually discretized into tesseroids (i.e., spherical prisms) to consider the influence of the curvature of planets in global or large-scale problems. Traditional gravity forward modeling methods in spherical coordinates, including the Taylor expansion and Gaussian–Legendre quadrature, are all based on spatial domains, which mostly have low computational efficiency. This study proposes a high-efficiency forward modeling method of gravitational fields in the spherical harmonic domain, in which the gravity anomalies and gradient tensors can be expressed as spherical harmonic synthesis forms of spherical harmonic coefficients of 3D density distribution. A homogeneous spherical shell model is used to test its effectiveness compared with traditional spatial domain methods. It demonstrates that the computational efficiency of the proposed spherical harmonic domain method is improved by four orders of magnitude with a similar level of computational accuracy compared with the optimized 3D GLQ method. The test also shows that the computational time of the proposed method is not affected by the observation height. Finally, the proposed forward method is applied to the topography correction of the Moon. The results show that the gravity response of the topography obtained with our method is close to that of the optimized 3D GLQ method and is also consistent with previous results.

Giant impact on early Ganymede and its subsequent reorientation

Ganymede has an ancient impact structure called a furrow system. The furrow system is the largest impact structure in the outer solar system, and the impact should have significantly affected Ganymede’s early history; however, its effects are poorly understood. No attention has been given to the center of the furrow system coinciding with Ganymede's tidal axis, indicating that mass redistribution induced by the furrow-forming impact caused a reorientation (true polar wander) of Ganymede. We propose that the impact ejecta created a mass anomaly that reoriented the impact site toward the tidal axis. We found that an impactor with a radius of 150 km and an incidence angle between 60° and 90° most accurately reproduces the current location of the furrow system. We predict that future explorations would reveal remnant topographic profiles or gravity anomalies associated with the furrow-forming impact and reorientation. Additionally, various possible explanations for the reorientation of Ganymede, such as an impactor-origin mascon beneath the basin or a thickness variation in the lithosphere, should be studied.

Improved Bathymetry Estimation Using Satellite Altimetry-Derived Gravity Anomalies and Machine Learning in the East Sea

This study aims to improve the accuracy of bathymetry predicted by gravity-geologic method (GGM) using the optimal machine learning model selected from machine learning techniques. In this study, several machine learning techniques were utilized to determine the optimal model from the performance of depth and gravity anomalies. In addition, a tuning density contrast calculated from satellite altimetry-derived free-air gravity anomalies (FAGAs) was applied to estimate enhanced bathymetry. By comparison with shipborne depth, the accuracy of the bathymetry estimated by using satellite altimetry-derived FAGAs and machine learning was evaluated. The findings reveal that the bathymetry predicted by the optimal machine learning using the Gaussian process regression and the GGM with a tuning density contrast can enhance the accuracy of 82.64 m, showing an improvement of 67.40% in the RMSE at shipborne depth measurements. Although the tuning density is larger than 1.67 g/cm3, bathymetry using satellite altimetry-derived FAGAs and machine learning can be effectively improved with higher accuracy.

The performance of Tiangong-2 InIRA in gravity anomaly recovery from fused conventional nadir-observing altimeter data

Satellite altimetry has emerged as the primary data source for deriving marine gravity, owing to advancements in satellite altimetry technologies. The Tiangong-2 interferometric imaging radar altimeter (TG2 InIRA) is a new generation wide-swath altimeter (WSA) launched by China on 15 September 2016. TG2 InIRA employs a short-baseline interferometry measurement method at a small incidence angle, thereby enabling the precise measurement of wide-swath sea surface heights (SSHs). The present study demonstrates the efficacy of utilizing data from the TG2 InIRA in conjunction with data from the CryoSat-2 (CS2) satellite to recover marine gravity anomalies. In the Northwest Pacific region (15°N ∼ 25°N, 140°E ∼ 150°E), the TG2 InIRA data were filtered to a 2 km × 2 km grid and any anomalous values were excluded using a mean sea surface model. Deflections of the vertical (DOVs) were calculated using the remove-restore technique, and the inverse Vening-Meinesz formula was applied to invert the gravity anomaly. A power spectral density analysis was conducted to assess variations in gravity anomalies at ship locations in the frequency domain. The results, based on the SIO V31.1 (DOV) model, indicate that the gridded vertical deflections derived from CS2 and TG2 InIRA data achieve more balanced accuracy in the east–west and north–south directions. The integration of TG2 InIRA and CS2 data yielded gravity results exhibiting a root mean square (RMS) of 0.519 mGal lower than those derived from TG2 InIRA data alone, and 0.034 mGal lower than those derived from CS2 data. These findings were derived from shipborne gravity data. Furthermore, the inversion of gravity anomalies from TG2 InIRA demonstrated an enhanced capability in the reduction of high-frequency gravity noise.

Spatial variations in the effective elastic thickness of the Indian Ocean lithosphere

The Indian Ocean lithosphere is a complex assemblage of large igneous provinces, seamounts, plateaus and ridges of different loading ages and tectono-thermal evolution. As a proxy for the strength of tectonic plates, effective elastic thickness (Te) illustrates the relationship between surface deformation and lithospheric rheology of the diverse provinces in response to long-term tectonic processes. Mapping the spatial variations in lithospheric rheology can aid in understanding the detailed tectono-thermal history of the Indian Ocean. In this paper, we perform an assessment of the spatial variation of Te for the Indian Ocean from the inversion of the real free-air admittance between free-air gravity anomalies and bathymetry corrected for the effect of density variations within sediments using a continuous wavelet spectral analysis. Incorporating the effect of sediments substantially reduces Te estimates and better corresponds with the tectonic units in the study region. The results show low overall Te over the Indian Ocean attributed to magmatism and temperature during a multistage opening process. We further demonstrate that temperature controls the strength of warm and young oceanic lithosphere, evidenced by the positive correlation between Te and geothermal proxies. Finally, moderately low Te values at the Southwest Indian Ridge suggest a relatively cold ultraslow lithosphere with sparse magmatism compared to typical mid-ocean ridges.

Resolution analysis of the gravity survey network in the middle and south sections of Tan-Lu fault and recent changes in the gravity field

In this paper, we computed the fractal dimension of three survey areas within the central and southern sections of the Tan-Lu fault zone using fractal analysis. Subsequently, simulations were conducted to analyze the gravity response under a forward model of equivalent density changes. Additionally, we thoroughly investigated the seismic monitoring capabilities of the gravity network in the central and southern regions of the Tan-Lu fault. Expanding on these analyses. Recent gravity field variations were examined in the mid-southern segment of the Tan-Lu fault zone and its surrounding areas from 2013 to 2023. The results indicate that the observation capabilities of the northern network in the study area outperform those of the southern gravity network, with the northern network demonstrating a more evenly distributed coverage. The optimal gravity anomaly recovery effect for the entire study area is achieved at a resolution of 0.5° × 0.5°. With an equivalent observable signal in the range of 30 × 10−8 m/s2 to 40 × 10−8 m/s2, the spatial resolution of the gravity network's field source is estimated to be approximately 55 km. From 2013 to 2023, a significant positive change has been observed in the gravity field within the study area. The Tan-Lu fault zone plays a crucial role in governing the crustal movement in this region, with the dextral strike-slip movement trend of the fault persisting. Small earthquakes occur more frequently in the southern section of the fault zone, while strong earthquakes are less common. The alignment of gravity field changes with the fault strike indicates ongoing activity in the fault zone without any signs of locking. In the central segment of the Tan-Lu fault zone in the Shandong region, there appears to be a weaker correlation between gravity field changes and fault trends. This discrepancy may suggest that the area is locked, resulting in the accumulation of stress and strain. It is imperative to monitor the continuous evolution of the gravity field in this region to gain insights into potential seismic risks.

Deciphering the 3D density structure using gravity data in the Vitória-Trindade Ridge, South Atlantic-Brazil

The Vitória-Trindade Ridge, situated in the central South Atlantic Ocean, is the longest aseismic ridge. The ridge comprises alkaline volcanic formations, including seamounts such as Davis, Colúmbia, Congress banks, Jaseur, Montague, and Vitória. The VTR resulted from a mantle plume, making it structurally complex. A study was conducted to investigate the structure of the VTR using gravity anomalies and bathymetric data. The study revealed the 3D structure of seamounts in terms of their density, depth, and volume. The results showed that the volume of seamounts with depth of VTR ranges from 10 to 14 km. Additionally, the uneven distribution of density contrast indicates the complex structure of the VTR region.