Gravity Field Data Research Articles

Inhomogeneous Magnetization of Tyrrhenian Seamounts Revealed From Gravity and Magnetic Correlation Analysis

AbstractWe perform a joint analysis of gravity and magnetic data sets in the Tyrrhenian Sea region to infer the rock physical properties of several volcanic seamounts. We propose a moving‐window application using Poisson's theorem, which relates the total gradient of the magnetic field to the total gradient of the first‐order vertical derivative of the gravity field data. In volcanic environments, where strong intensity of remanent magnetization is expected, the total gradient of the magnetic field is particularly useful since it is almost independent on the direction of the total‐magnetization. The moving‐window approach resulted necessary due to the heterogeneous magnetization distribution of the volcanoes. First, we perform synthetic tests based on realistic seamount models which exhibit inhomogeneous magnetization intensity and orientation. Using the total gradients, we demonstrate that our approach can provide an appropriate magnetization‐to‐density ratio in different subareas of seamounts. The results of the correlation analysis for the Palinuro, Marsili, Vavilov, and Magnaghi seamounts provide interesting information on the variability of magnetization associated with different epochs of formation and demagnetization effects due to hydrothermal alteration processes.

Quenching of zonal winds in Jupiter’s interior

Gravity and magnetic field data obtained by the Juno mission show that Jupiter's strong zonal winds extend a few thousand kilometers into the interior, but are quenched above the level where the electrical conductivity becomes significant. Here, we extend a simple linearized model [Christensen et al., Astrophys. J. 890, 61 (2020)] that explains the braking of the jets by the combination of stable stratification and electromagnetic effects. We show that in the inviscid limit, the process is essentially governed by a single parameter, which we call the MAC-number (for the forces acting on the flow-Magnetic, Archimedian, and Coriolis). The predictions for the drop-off of the zonal winds agree well with results from 3D-convection models. We run calculations that take the full range of density and electrical conductivity variations in the top 5,600 km of Jupiter into account. In order to satisfy constraints on the power driving the jets and on their effect on Jupiter's magnetic field, the top of the stable layer and the region where the jet velocity drops sharply must be near 2,000 km depth. The dissipation associated with quenching of the jets increases toward the poles, which can partly explain why the jets near [Formula: see text]20[Formula: see text] are faster than those at higher latitude.

Regularization of vertical derivatives of potential field data using Morozov's discrepancy principle

AbstractThe calculation of the vertical derivatives of potential field methods can be carried out in a stable manner by Tikhonov regularization, but this procedure requires the appropriate selection of a regularization parameter. For this purpose, we introduce a criterion based on Morozov's discrepancy principle that uses a preliminary approximation given by the vertical derivative of the smoothed data. The smoothing may be performed by a physical or a mathematically based low‐pass filter. The filtered data are computed only for estimating the regularization parameter; once it is found, we evaluate the regularized vertical derivative from the original data (not from the smoothed one) in the frequency domain. We verified from experiments with noise‐corrupted synthetic data, as well as gravity and magnetic field data, that the regularized vertical derivative has about the same smoothness as the one obtained from filtered data, but true anomalies are more easily distinguished from noise and the shapes of the anomalies are better preserved.

Joint Gravity and Magnetic Inversion Using CNNs’ Deep Learning

Enhancing the reliability of inversion results has always been a prominent issue in the field of geophysics. In recent years, data-driven inversion methods leveraging deep neural networks (DNNs) have gained prominence for their ability to address non-uniqueness issues and reduce computational costs compared to traditional physically model-driven methods. In this study, we propose a GMNet machine learning method, i.e., a CNN-based inversion method for gravity and magnetic field data. This method relies more on data-driven training, and in the prediction phase after the model is trained, it does not heavily depend on a priori assumptions, unlike traditional methods. By forward modeling gravity and magnetic fields, we obtain a substantial dataset to train the CNN model, enabling the direct mapping from field data to subsurface property distribution. Applying this method to synthetic data and one-field data yields promising inversion results.

Gravimetric and morpho-structural analyses in the superhot geothermal system Los Humeros: an example from central Mexico

The influence of deep and regional geological structures is becoming increasingly important in superhot geothermal systems due to their proximity to the transition between brittleness and ductility. In the Los Humeros geothermal field in Mexico, where subsurface fluids reach temperatures of over 350 °C, the surface structures resulting from the collapse of calderas have so far only been interpreted at the local scale. The aim of this work is to place the recent tectonic and volcano-tectonic geomorphologic evolution and structures in the Los Humeros volcanic area in a regional context. NE- and NW-striking dominant structures resulting from a morpho-structural analysis on a regional scale are confirmed by negative and positive anomalies, respectively, after Butterworth filtering of gravity field data with different wavelengths over a local area of about 1000 km2. By analyzing the slip and dilation trends of the observed directions, we show the relevance of the regional context for reservoir exploration. The magnitudes of the principal stresses we estimate indicate a trans-tensional fault regime, a combination of strike-slip and normal faulting. The structures derived from the gravity and morpho-structural analyses, which are parallel to the maximum horizontal stress, have the highest potential for tensile and shear failure. Therefore, the corresponding negative gravity anomalies could be related to fracture porosity. Consequently, we hypothesize that these structures near the transition between brittleness and ductility control fluid flow in the Los Humeros geothermal field.

An experimental investigation on the discrete Poisson’s upward continuation of anomalous gravity field data

An experimental investigation on the discrete Poisson’s upward continuation of anomalous gravity field data

High-Resolution Gravity Measurements on Board an Autonomous Underwater Vehicle: Data Reduction and Accuracy Assessment

Gravity on Earth is of great interest in geodesy, geophysics, and natural resource exploration. Ship-based gravimeters are a widely used instrument for the collection of surface gravity field data in marine regions. However, due to the considerable distance from the sea surface to the seafloor, the spatial resolution of surface gravity data collected from ships is often insufficient to image the detail of seafloor geological structures and to explore offshore natural minerals. Therefore, the development of a mobile underwater gravimetry system is necessary. The GraviMob gravimeter, developed for a moving underwater platform by Geo-Ocean (UMR 6538 CNRS-Ifremer-UBO-UBS), GeF (UR4630, Cnam) and MAPPEM Geophysics, has been tested over the last few years. In this study, we report on the high-resolution gravity measurements from the GraviMob system mounted on an Autonomous Underwater Vehicle, which can measure at depths of up to several kilometres. The dedicated GraviMob underwater gravity measurements were conducted in the Mediterranean Sea in March 2016, with a total of 26 underwater measurement profiles. All these measurement profiles were processed and validated. In a first step, the GraviMob gravity measurements were corrected for temperature based on a linear relationship between temperature and gravity differences. Through repeated profiles, we acquired GraviMob gravity measurements with an estimated error varying from 0.8 to 2.6 mGal with standard deviation after applying the proposed temperature correction. In a second step, the shipborne gravity data were downward continued to the measurement depth to validate the GraviMob measurements. Comparisons between the corrected GraviMob gravity anomalies and downward continued surface shipborne gravity data revealed a standard deviation varying from 0.8 to 3.2 mGal and a mean bias value varying from −0.6 to 0.6 mGal. These results highlight the great potential of the GraviMob system in measuring underwater gravity.

High-resolution temporal gravity field data products: Monthly mass grids and spherical harmonics from 1994 to 2021

Since April 2002, Gravity Recovery and Climate Experiment (GRACE) and GRACE-FO (FollowOn) satellite gravimetry missions have provided precious data for monitoring mass variations within the hydrosphere, cryosphere, and oceans with unprecedented accuracy and resolution. However, the long-term products of mass variations prior to GRACE-era may allow for a better understanding of spatio-temporal changes in climate-induced geophysical phenomena, e.g., terrestrial water cycle, ice sheet and glacier mass balance, sea level change and ocean bottom pressure (OBP). Here, climate-driven mass anomalies are simulated globally at 1.0° × 1.0° spatial and monthly temporal resolutions from January 1994 to January 2021 using an in-house developed hybrid Deep Learning architecture considering GRACE/-FO mascon and SLR-inferred gravimetry, ECMWF Reanalysis-5 data, and normalized time tag information as training datasets. Internally, we consider mathematical metrics such as RMSE, NSE and comparisons to previous studies, and externally, we compare our simulations to GRACE-independent datasets such as El-Nino and La-Nina indexes, Global Mean Sea Level, Earth Orientation Parameters-derived low-degree spherical harmonic coefficients, and in-situ OBP measurements for validation.

Adaptive Space–Location-Weighting Function Method for High-Precision Density Inversion of Gravity Data

Underground 3D density variation can be obtained via the inversion of gravity data, which is a very important basis for structural division, oil and gas structure definition, and mineral resource evaluation. A depth-weighting function is usually introduced as a structural constraint in density inversion to solve the skin effect. We propose an adaptive space–location-weighting (ASW) function for gravity field data to improve the resolution of the inversion, which adds the position and depth information provided by the DEXP method to form a new weighting function. The weighting function is partitioned according to the horizontal distribution of the source and can effectively improve the resolution of field sources with different positions and different depths. The results of model tests have shown that the ASW function method can significantly improve the precision and resolution of density inversion results and has good noise immunity. The ASW method was applied to interpret the real gravity data of a mining area in Shandong, and we speculated potential mineralization based on the inversion results, which corresponded well with the logging results.

Evaluation of geological structures and geothermal resources in the North Tanzania Volcanic area using remote sensing and gravity data analysis

Problems Statement and Purpose. Northern Tanzania Volcanic terrain has been a subject of evaluation for geothermal potential in the last four decades. The region is characterized by Neogene to Recent volcanic and tectonic activities. This preliminary study based on remote sensing, water chemistry, gravity data, geological structures and volcanic centers distribution reports the geothermal manifestations identified and discusses the implications on geothermal fluid pathways. Oxygen-hydrogen isotope data from water samples indicate that there were involved in the hydrothermal system. Tectono-Volcanic Structures. The Northern Tanzania Divergence (NTD) area characterized by Neogene to Recent volcanic and tectonic activities. Recent volcanic and tectonic activities are ash cone and lava dome eruption at the floor of Meru crater a century ago, dyke intrusion and volcanic eruption south of Gelai volcano, and Oldoinyo-Lengai volcano, respectively. Fumarolic activities and hot springs are dominant in a relatively young volcanic area to the north-eastern and northern part of the NTD. Data and Methods. Shuttle Radar Topography Mission (SRTM), Landsat 8 Operational Land Imager (OLI) image, water isotope analysis and gravity data were used to extract and analyze the surface and subsurface geological lineaments and map the hydrothermal alteration zones in the study area. The hydrothermal alteration is used to evaluate and identify the permeable structures. Analysis and interpretation of the length and trends of extracted lineaments were used to investigate the tectonic evolution. Geological map of a study area was digitized from the existing geological maps and the age of rocks to delineate volcanic activity and associated lineaments based on the age of the lithological domain. Digital image processing was applied to enhance the visual interpretation. Gravity data were used to give insight into the subsurface structure in the study area. Results and Discussion. The higher δ 18O values and large deviation from meteoric water lines suggest that is due to the interaction of fluids with host rocks at elevated temperatures. These are consistent with open structures that act as conduits for fluid flow. The potential field gravity data reveal a basin-like structure trending in the NNW direction. The gravity data show that the basement units gradually deepen towards the central part and that it is controlled by two main fault systems that trend N-S and NW-SE respectively. The gravity data presented here provides new constraints on the tectonic evolution and geothermal resources of the study area.

Crustal structure of the Tian Shan Orogen and its adjacent areas inferred from EIGEN-6C4 gravity field data

The study of crustal structure plays a crucial role in understanding the tectonic development and seismicity of orogens. In this contribution, we analyzed the gravitational responses caused by the subsurface structure of the Tian Shan orogen using global EIGEN-6C4 gravity field data. The quality of the EIGEN-6C4 gravity data in the eastern Tian Shan area was evaluated, and we employed the wavelet multi-scale analysis method in combination with an existing P-wave velocity model to reveal the structures at different scales. The gravity Moho of the study area that best fits the seismological Moho was obtained from the filtered third-order wavelet approach component after several tests using different reference depths and density contrasts. The results reveal significant lateral discontinuities in the lower crust and upper mantle structures across the eastern and western Tian Shan orogens. The Moho beneath the western Tian Shan is about 9 km deeper compared to the eastern Tian Shan. Towards the Tarim and Junggar basins, the Moho decreases to about 45 km in depth. The analysis of the isostatic anomaly suggests that the eastern and western Tian Shan possess varying capabilities for isostatic adjustment. Furthermore, it indicates a gradual weakening of mantle upwelling in the Tian Shan orogen from west to east, implying that different stages of mantle dynamics may have occurred in the western and eastern Tian Shan. The north Tarim fault, Nikolaev-North Nalati fault, and Kazakh fault serve as channels that control magma upwelling in the Tian Shan orogen. The distribution of earthquakes differs between the eastern and western Tian Shan, with the western Tian Shan exhibiting greater gravitational potential energy. These variations between the eastern and western Tian Shan may be attributed to the clockwise rotation of the Tarim craton.

A deep CNN-LSTM model for predicting interface depth from gravity data over thrust and fold belts of North East India

Geological interface depth modeling from the gravity field data is crucial for the exploration of oil and gas, mapping of sediment-basement interfaces and many other geological modeling studies. Two-dimensional radially averaged power spectrum analyses yield ensemble mean depth from the gravity anomaly which lacks information away from the focus of the depth point modeled. This paper aims to introduce adeep learning techniquecombining a convolutional neural networkand long short-term memory network (CNN-LSTM) with variogram modeling to model interface depths using geospatial coordinates, Bouguer gravity anomaly and altitude variations data over the thrust and fold belts of North-East India. Prior to actual data analysis, the method is tested on a synthetic model. After the optimal network selection with the ‘Relu’ transfer function and ‘Adam’ optimization, the robustness of the CNN-LSTM model is examined and the test results show that the model is robust up to the data assorted with moderate level (∼20%) of correlated noise. The CNN-LSTM model detects important geological structures, subsurface faults, and thrust zones namely, Kohima Synclinorium, Naga and Disang thrust, and Eastern boundary thrust. Comparative results suggest that the CNN-LSTM is skillful based on the mean-squared error (MSE) between the observation and the prediction (MSECNN-LSTM ∼ 0.014) relative to the other machine learning models (Adaptive-Neuro-Fuzzy-Inference System (MSEANFIS ∼ 0.020), Random Forest with Extra Tree(MSERF ∼ 0.075), Support Vector Regression (MSESVR ∼ 0.025), Bayesian Neural Network(MSEBNN ∼ 0.116), Convolutional Neural Network(MSECNN ∼ 0.022), Long Short-Term Memory Network(MSELSTM ∼ 0.023), and Gaussian Process(MSEGP ∼ 0.095)) used in the study. We, therefore, conclude that the proposed approach is robust enough to model the various geological interface depths precisely with appropriate input data of geospatial coordinates, Bouguer gravity anomaly and altitude variation. The approach used here has potential and could be explored further in other complex geological provinces around the world.

Modified non-local means: A novel denoising approach to process gravity field data

Abstract Gravity measurement is a basic geophysical method for non-destructive exploration of mineral resources and hydrocarbons and also for geological studies. The quality of gravity data measured is constantly degraded by a low signal-to-noise ratio. Noise attenuation thus plays a vital role in processing and interpreting gravity field data. The non-local means (NLM) filtering was first and successfully introduced to attenuate randomly distributed noise situated in seismic records in geophysical community. However, less attention has been drawn to apply NLM to denoise potential field data, since the success of NLM is guaranteed by carefully tuned parameters and large computational costs. Here we propose the modified NLM (MNLM), based on unweighted Euclidean distance and integral image method, to denoise gravity datasets. The unweighted Euclidean distance is used to reduce the uncertainty and complexity of tuning the control parameters involved, and the integral image strategy is applied to avoid the enormous computational cost of the NLM. Since these filtering properties are desirable to mitigate noise in gravity datasets, we test and evaluate the MNLM filter on noisy synthetic gravity models with uniform and normal distributions and on real data from the Mariana trench and Slovakia, and compare it to other standard and representative denoising filters. The results on synthetic and field datasets confirm the high speed of this modified algorithm and show that, it removes noise most effectively while clearly preserving and not blurring structural details with less tuned parameters. The MNLM filter can therefore be considered as a promising and novel algorithm for denoising gravity data.

A framework of gravity field online modeling and trajectory optimization in asteroid soft-landing mission scenarios

Focused on asteroid soft-landing mission scenario, this paper investigates the methods of online gravity field modeling and trajectory optimization. Since the gravity field near asteroids and its surface topography are irregular, accurate asteroid soft-landing can be a challenging problem to solve, especially in the case of limited gravity data. With an actual asteroid soft-landing mission in mind, this study constructs a dataset based on the gravity field data collected in the limited area through which a soft-landing probe passed, and conducts online modeling of the asteroid inhomogeneous gravity field at the same time, using a transfer learning deep neural network (TL-DNN). To achieve fuel optimization and an accurate landing, a fast trajectory optimization method based on Bezier shape approach is implemented to achieve fast online robust re-trajectory optimization. Numerical simulation results show that the adopted TL-DNN can quickly reconstruct the gravitational field near the asteroid online, based only on the limited gravity data collected by the probe. Compared with the situation without online gravity field reconstruction and multiple online trajectory optimization, the proposed online gravity field modeling and re-trajectory optimization method can reduce the final position and velocity error between the multi-planned ideal optimal trajectory and the actual trajectory from 2396.367 m and 3.870 m/s to 14.152 m and 0.075 m/s, respectively. In other words, the soft-landing accuracy is improved by about two orders of magnitude.

Results from the Third QDaedalus Astrogeodetic System Observation Campaign in the Mountainous Terrain of the Surses Region in Switzerland

This study presents the results of an astrogeodetic survey campaign conducted in the mountainous terrain of the Surses region in Switzerland. In our third astrogeodetic campaign using the QDaedalus system, we observed new deflections of the vertical (DoV) using three astrogeodetic systems. These observations were used to validate DoV data derived from the Global Gravity Model GGMplus and the Swiss Geoid model CHGeo2004. Astrogeodetic observations were conducted at 15 benchmarks (BMs) along the astrogeodetic profile over five nights in June 2021 at elevations ranging from 1,185 to 1,800 m and a station spacing of about 1 km. This is the first time two TS60 total station-based QDaedalus systems and one zenith telescope-based COmpact DIgital Astrometric Camera (CODIAC) system were used together for an astrogeodetic observation campaign. The standard deviations (SDs) of the QDaedalus system data for each session were 0.04″–0.22″ and 0.01″–0.20″ for the N–S and E–W components, respectively, while the SDs of the CODIAC system for each session were 0.02″ for both components. These high quality data were compared to DoV data derived from GGMplus and CHGeo2004. The N–S components from GGMplus exhibited large residuals ranging from −2.31″ to 1.75″, while the E–W component residuals are from −0.27″ to 1.80″. The residuals from CHGeo2004 range from −0.60 to 1.21 for the N–S components and −1.01 to 0.32 for the E–W components. These results show that the derived DoV data from CHGeo2004 are closer to the observed DoV and more accurate than the global GGMplus model that does not incorporate local gravity field data. The first and second astrogeodetic observation campaigns were conducted in the coastal terrain of Istanbul, Turkey and in the flat terrain in the Munich region, Germany, respectively. In this study, we provide an overall comparison of these previous results to the GGMplus residuals. Our latest results show that the GGMplus model is of higher quality in the Surses mountainous terrain than in the coastal terrain of Istanbul, while it is of lower quality than in the flat terrain of the Munich region.

Forcing of slow density waves in the C ring by Saturn's quasi-toroidal normal modes

We calculate Saturn's quasi-toroidal normal modes of azimuthal order three for several candidate models for the interior stratification. We propose that these oscillations are responsible for exciting a class of slowly propagating spiral density waves observed in the C ring, possessing the same azimuthal symmetry and pattern speeds, that cluster near the rotation rate of the planet. Both prograde and retrograde propagation relative to the planetary rotation are observed. We find that we can construct interior models for which the pattern speeds of the normal modes align with those of a majority of the slow density waves. Excitation of the retrograde density waves requires retrograde toroidal modes with matching pattern speeds. We find that in order to support such modes, the interior model must include a layer of positive static stability in the molecular hydrogen envelope. The model that performs best places the peak of the static stability at a fractional radius of 0.85, near the radius of the semi-conductive layer where Saturn's zonal jets are inferred to decay. The properties of the toroidal modes are less sensitive to the stratification in the deep interior, hence our results neither confirm nor contradict the presence of a dilute core. When differential rotation is included in the form of zonal winds based on analysis of Cassini gravity and magnetic field data, we find that several toroidal modes become inertially unstable, raising the possibility that this instability is the principal mechanism for their excitation. However, while we are able to construct interior models that align the pattern speeds of a few unstable retrograde modes with those of prominent density waves in the ring, the same models fail with respect to the unstable prograde modes. Consequently, the significance of the instability mechanism remains an open question. Our results suggest that the interaction of Saturn's quasi-toroidal modes with the C ring provides an important source of new, complementary constraints on the planet's internal stratification and that further study of these modes is warranted.

Early Cretaceous Tectonostratigraphic Evolution of the Southern Tunisian Margin Based on Gravity, Seismic and Potential Field Data: New Insights into a Geodynamic Evolution in a Tethyan and Mesogean Rifting Context

Early Cretaceous Tectonostratigraphic Evolution of the Southern Tunisian Margin Based on Gravity, Seismic and Potential Field Data: New Insights into a Geodynamic Evolution in a Tethyan and Mesogean Rifting Context

A Set of Geophysical Fields for Modeling of the Lithosphere Structure and Dynamics in the Russian Arctic Zone

This paper presents a set of various geological and geophysical data for the Arctic zone, including some detailed models for the eastern part of the Russian Arctic zone. This hard-to-access territory has a complex geological structure, which is poorly studied by direct geophysical methods. Therefore, these data can be used in an integrative analysis for different purposes. These are the gravity field, heat flow, and various seismic tomography models. The gravity field data include several reductions calculated during our preceding studies, which are more appropriate for the study of the Earth’s interiors than the initial free air anomalies. Specifically, these are the Bouguer, isostatic, and decompensative gravity anomalies. A surface heat flow map included in the dataset is based on a joint inversion of multiple geophysical data constrained by the observations from the International Heat Flow Commission catalog. Available seismic tomography models were analyzed to select the best one for further investigation. We provide the models for the sedimentary cover and the Moho depth, which are significantly improved compared to the existing ones. The database provides a basis for qualitative and quantitative analysis of the region.

A New Combined Technique for Solving Nonlinear Gravity Problems from Planetary Topography, Gravity Field Data, and Crustal Thickness Using a Throwing off Algorithm

A New Combined Technique for Solving Nonlinear Gravity Problems from Planetary Topography, Gravity Field Data, and Crustal Thickness Using a Throwing off Algorithm

Saturn's Seismic Rotation Revisited

Normal mode seismology is a promising means of measuring rotation in gas giant interiors, and ring seismology presents a singular opportunity to do so at Saturn. We calculate Saturn’s normal modes of oscillation and zonal gravity field, using nonperturbative methods for normal modes in the rigidly rotating approximation, and perturbative methods for the shifts that Saturn’s deep winds induce in the mode frequencies and zonal gravity harmonics. The latter are calculated by solving the thermogravitational wind equation in an oblate geometry. Comparing many such models to gravity data and the frequencies of ring patterns excited by Saturn’s normal modes, we use statistical methods to estimate that Saturn’s cloud-level winds extend inward along cylinders before decaying at a depth 0.125–0.138 times Saturn’s equatorial radius, or 7530–8320 km, consistent with analyses of Cassini’s gravity and magnetic field data. The seismology is especially useful for pinning down Saturn’s poorly constrained deep rotation period, which we estimate at 2π/ΩS = 634.7 minutes (median) with a 5/95% quantile range of 633.8–635.5 minutes. Outstanding residuals in mode frequencies at low angular degree suggest a more complicated deep interior than has been considered to date. Smaller but still significant residuals at high angular degrees also show that our picture for the thermal, composition, and/or rotation profile in Saturn’s envelope is not yet complete.