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Greece is a country structured by land, several islands and sea. A regional gravity model of such a country demands the involvement of several types of gravity data in order to cover all its territory. In this paper, we present the development of a regional combined gravity model of Greece and its surrounding area, by integrating terrestrial, marine (shipborne and altimetry-derived gravity data) and satellite data (GOCE and GRACE data). These kinds of data, especially the terrestrial and marine ones, have passed quality and validation control since they were collected from different organizations, which means that they probably have been acquired and processed with different parameters and formulas. Following that, their integration was accomplished with the application of the least-squares collocation (LSC). Therefore, a newly combined regional gravity model of Greece and its surrounding areas has been developed. This gravity model can be valuable for regional geological and geophysical studies of Greece, since it provides homogeneously the distribution of Complete Bouguer anomaly (CBA) all over Greece, with updated gravity data and in good agreement with the initial gravity datasets that have been used.

The drift rate of relative gravimeters differs from time to time and from meter to meter. Furthermore, it is inefficient to estimate the drift rate by returning them frequently to the base station or stations with known gravity values during gravity survey campaigns for a large region. Unlike the conventional gravity adjustment procedure, which employs a linear drift model, we assumed that the variation of drift rate is a smooth function of lapsed time. Using this assumption, we proposed a new gravity data adjustment method by means of objective Bayesian statistical inference. Some hyper-parameters were used as trade-offs to balance the fitted residuals of gravity differences between station pairs and the smoothness of the temporal variation of the drift rate. We employed Akaike’s Bayesian information criterion (ABIC) to estimate these hyper-parameters. A comparison between results from applying the classical and the Bayesian adjustment methods to some simulated datasets showed that the new method is more robust and adaptive for solving problems caused by irregular nonlinear meter drift. The new adjustment method is capable of determining the time-varying drift rate function of any specific gravimeter and optimizing the weight constraints for every gravimeter used in a gravity survey. We also carried out an error analysis for the inverted gravity value at each station based on the marginal distribution. Finally, we used this approach to process actual gravity survey campaign data from an observation network in North China.

We present depth estimates and structural information, using aeromagnetic and gravity data, relating to the basement near the southwestern margin of the lower Benue trough between, and including, the Anambra basin and Afikpo syncline. The basement depth is calculated on the basis of the scaling spectral method, Euler deconvolution, and source parameter imaging (SPI) applied to gridded aeromagnetic data and 2D gravity modeling across the three main tectonic features (i.e., Anambra basin, Abakaliki anticlinorium, and Afikpo basin/syncline). We interpreted basement depths between 8.4 and 9.1 km within the Anambra basin, 3.1 and 9.8 km within the Abakaliki anticlinorium, and 3.8–8.5 km within the Afikpo basin/syncline. The magnetic basement depths estimated from the three methods applied on the aeromagnetic data are in good agreement with each other and that provided by the 2D gravity model which produced a reliable picture of the basement relief in the region. We also mapped and interpreted magnetic lineaments/faults in the region. Three well-defined magnetic lineaments/faults (NE–SW, ENE–WSW, and WNW–ESE) correlate very closely with the trends of geological structures of the Pan-African plate which predated the compressional folds and faults within the Cretaceous sediments and strike-slip movements during the separation of Africa and South America.

To understand the influence of sea ice on shipborne gravity measurements and the accuracy of the satellite-altimetry-derived gravity field in the Arctic Ocean, we compared shipborne gravity measurements with those obtained from satellite altimetric gravity measurements. The influence of sea ice on the shipborne gravity measurements was mainly concentrated in the 0–6 km wavelength range, and the standard deviation of the noise amplitudes was 2.62 mGal. Compared to ice-free regions, the accuracies in the region with floating ice were reduced by 13% for DTU21 and 6% for SV31. Due to the influence of sea ice, satellite altimetric gravity data lose significant information in the 9–12 km wavelength range. The coherence curve of the shipborne gravity with bathymetry was nearly the same as that of the satellite altimetric gravity. The satellite data contain nearly all of the significant information that is present in the shipborne data. The differences between the shipborne and satellite gravity data are small and can be used to study the crustal structure of the Arctic.

Compilation of gravity maps from Romania and Bulgaria provided geophysical data with very good regional coverage, making possible enhanced data processing and cross-border geological interpretation of gravity data on the Moesian Platform. By merging the available gravity data into a unique dataset, a Bouguer gravity anomaly map of the Moesian Platform in Romania and Bulgaria was produced. When applying filtering techniques, the residual gravity anomaly map of the Moesian Platform provided valuable information on the Intramoesian Fault segments in both Romania and Bulgaria. Large and deep geological structures of the Moesian Platform were interpreted on gravity anomalies at crustal depths based on density contrasts as compared with the neighbouring background. Aeromagnetic data processing using different filtering methods in Oasis montaj software, resulted in a series of other magnetic maps and offered new possibilities of interpreting the available data.

The Cambay rift basin (CRB) is an intracratonic rift in the western part of India. The basin assumes great importance in petroleum exploration owing to the presence of thick hydrocarbon bearing sedimentary rocks. Previous investigations using deep seismic soundings (DSS), gravity and heat flow data reveal that the CRB is characterised by a thin crust, high heat flow and high density lower crust. In this study, a detailed crustal structure of the basin is presented by performing a 2.5D density modelling of the EIGEN-6C4 gravity data. Present study attempt to find a plausible explanation for the variation in the Bouguer anomaly (BA) values from +20 to −50 mGal within the basin. It refined the crustal model that is constrained using results from radial average power spectrum (RAPS) analysis of gravity data along with previous seismological and geophysical studies, which reveals that the values of average sedimentary and Deccan Traps thickness are in the order of 4–5 and 1.5–3 km, respectively, along the rift. It also presents possible evidences for a high density underplated layer of thickness 7–15 km along the central part of the CRB. To study the deep-seated features, upward continuation of the BA is carried out at heights of 30, 40 and 50 km. The extension of underplating layer is noticed in the present crustal model and in the upward continued BA in the western part, while it merges with the Moho in eastern part of the CRB. The Moho depths, varying from 31 to 37 km, are found to be shallower inside the CRB than the surroundings. It is inferred that the high BA values in the basin are due to the combined effect of the high density underplated layer in the lower crust and a shallow Moho.

As the west rim of an ejective fold zone, the Huaying Mountain fault zone (HMFZ) in the eastern Sichuan Basin (SB) plays an important role in the tectonic evolution of the SB. The distribution and characteristics of HMFZ are strongly associated with tectonic activities and have greatly impacted the distribution of oil and gas reservoirs. However, its distribution and characteristics have remained poorly understood due to a lack of geophysical data, especially areal gravity survey and magnetotellurics (MT) survey, which are admittedly advantageous for detecting the edges of geological structures. Therefore, we carried out the ground geophysical surveys that areal gravity survey and MT survey, and acquired 1:250,000-scale real gravity data and MT data for the first time in this area. Optimized edge-detection methods were adopted to process the areal gravity data, allowing us to characterize the planar distribution of faults more reliably and convincingly. We found that the southern HMFZ is well developed and primarily trends in NNE and NE, whereas the subordinate faults trend in N-S and W-E. Vertical information for the faults extracted using the improved depth from the extreme points method revealed that the fault dominantly dipped to the SE, which was consistent with the results of MT inversion. Based on the spatial distribution of the faults, we further discussed the gravity anomaly, fault distribution, Luzhou palaeo uplift, and the distribution and characteristics of oil-gas resources, and found the convincing evidence to analysis the distribution of oil and gas resources in this region.

The primary task of the IAG Sub-Commission on Gravity and Geoid in Africa is the development of the vertical reference surface (the geoid) for the entire African continent. For the practical solution of this boundary value problem, the available, arbitrarily distributed boundary data (gravity values) must be interpolated onto a regular grid for numerical reasons. In this paper it is explained in detail how to create this grid from the irregularly distributed point-gravity data. It is worth mentioning that this gravity database is not only used for geoid computation; it is also a stand-alone product used in earth sciences, as it reflects interesting geophysical signals. The gravity data available in this project are land and shipborne point gravity values as well as altimetry-derived gravity anomaly data. One challenge of preparing the homogeneous grid of gravity anomalies is caused by the inhomogeneous distribution of the observations and a lot of data gaps, especially on land. At these data gaps, gravity anomalies are provided on a so-called underlying grid from the GOCE DIR_R5 global reference model. One challenge in the framework of the least-squares prediction technique used is the determination of an empirical covariance function representing the behaviour of the irregularly distributed data points and the individual weights of the land, shipborne, and altimetry data and the underlying grid entering the process. A sophisticated filtering of the available gravity data is carried out to meet this challenge. The preprocessed data from the remove step are predicted to an equiangular 5′×5′ grid. Finally, a consistent restore step leads to the AFRGDB_V2.2 gravity database. The precision of the developed gravity database has been studied to assess the quality of the new product. The new AFRGDB_V2.2 gravity database is compared to the preceding one (AFRGDB_V2.0), which was generated using the window remove-restore technique.

The Ramgarh structure of south-east Rajasthan is currently established as an asteroid impact crater in India that was excavated in undeformed target sedimentary rocks of the Vindhyan Supergroup of Meso- to Neoproterozoic age. The size of this impact crater is debatable, and two opposing ideas of ~2.4 km and ~10 km diameter exist. The geomorphology of the crater has been re-evaluated using a Sentinel-2A false-coloured imagery of band combination 8, 4, 3 (spatial resolution 10 m) and Landsat-7 Satellite panchromatic imagery of Path/Row 146/042 and SRTM data (spatial resolution 30 m), along with satellite Bouguer gravity data downloaded from GGMplus besides our routine geological field work. Our observation on satellite imageries suggests that the geomorphologic high (~200 m) encircling the Ramgarh structure represents the actual crater’s rim which has a diameter of ~2.4 km. Our satellite gravity data show that the maximum negative Bouguer anomalies (–60 to –51 mGal) only coincide with this geomorphological high surrounding the Ramgarh structure, confirming this structural high of diameter ~2.4 km represents the rim of the crater.

Gravity anomalies play critical roles in geological analysis, geodynamic monitoring, and precise geoid modeling. Obtaining accurate gravity data is challenging, particularly in inaccessible or sparsely covered regions. This study evaluates machine learning (ML) methods—Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Ensemble of Trees—for predicting gravity anomalies in southeastern Kazakhstan and compares their effectiveness with traditional Kriging interpolation. A dataset, consisting of the simple Bouguer anomaly values, latitude, longitude, elevation, normal gravity, and terrain corrections derived from historical maps at a scale of 1:200,000, was utilized. Models were trained and validated using cross-validation techniques, with performance assessed by statistical metrics (RMSE, MAE, R2) and spatial error analysis. Results indicated that the Exponential GPR model demonstrated the highest predictive accuracy, outperforming other ML methods, with 72.9% of predictions having errors below 1 mGal. Kriging showed comparable accuracy and superior robustness against extreme errors. Most prediction errors from all methods were spatially associated with mountainous regions featuring significant elevation changes. While this study demonstrated the effectiveness of machine learning methods for gravity anomaly prediction, their accuracy decreases in complex terrain, indicating the need for further research to improve model performance in such environments.