Time-variable Gravity Signals Research Articles

GCMEbox: A MATLAB toolbox for extracting and analyzing common-mode errors from GNSS time series

High-precision global navigation satellite system (GNSS) time series data contain rich tectonic and non-tectonic motion information. However, such data also have common-mode errors (CME) related to spatiotemporal characteristics. CME will produce bias in accurate and reliable GNSS station positions and velocities estimations, ultimately deciphering erroneous geophysical signals. Currently, CME extraction methods can be divided into three categories: regional stack filtering, reference frame filtering, and statistical signal decomposition. The third method is widely used in geodetic data processing, including GNSS time series analysis, global time-varying gravity signal extraction, interferometric synthetic aperture radar (InSAR) time series analysis, etc. The demand for GNSS time series analysis software with interactive, visualization, and multifunctional features has also increased with the increase in GNSS stations. Therefore, we developed gCMEbox based on MATLAB. The gCMEbox’s primary functions include GNSS time series preprocessing, multiple statistical signal decomposition algorithms for extracting CME, GNSS time series analysis, visualization, velocity field construction, and generation of multiple time series post-processing products. Based on GNSS time series data, this study utilized the gCMEbox for data preprocessing, including format conversion, outlier removal, missing data recovery, etc. By applying statistical signal decomposition methods, gCMEbox successfully extracted CME. The primary objective of this study is to share this toolbox with the scientific community, providing a comprehensive tool for GNSS time series analysis and applications.

Detection of time variable gravity signals using terrestrial clock networks

The relativistic redshift between two earth-bound clocks can be interpreted in terms of gravity potential variation between the clock locations. A clock with a fractional frequency uncertainty of 10-18 is sensitive to a gravity potential variation of 0.1 m2/s2 or a height difference of 1 cm. Case studies for four regions affected by different mass change processes - Himalaya, Amazon, Greenland, and Fennoscandia - have been carried out. As the clocks rest on the deformable Earth’s surface, clock observations do not only include potential variations due to mass changes but also associated variations due to the vertical deformation of the land. For the simulations, vertical displacements were derived from real GNSS (Global Navigation Satellite Systems) measurements, and mass variations were computed from GRACE (Gravity Recovery And Climate Experiment) solutions. In the Himalayan region, seasonal variations with a maximum range of -0.20.2 m2/s2 were obtained. There, early and long-lasting precipitation patterns in North-East India and the gradual spreading towards the West can be observed by a dedicated clock network. For the Amazon region, seasonal variations with a maximum range of -0.50.5 m2/s2 observed by clocks also reveals the Amazon’s seasonal properties of annual rainfall variability at the North and South of the equator. The rainy season in the North of the equator is during the summer season from June to August, but from November to April in the South of the equator. The long-term trend of the ice mass loss in Greenland between 2004 and 2015 causes signals of potential variations of 1 m2/s2 that again can clearly be observed by clock measurements. Especially, the higher rates of mass variations in the west and south parts of Greenland can well be observed. The land uplift pattern of Fennoscandia due to the GIA (Glacial Isostatic Adjustment) can also be detected using optical clocks, however, the vertical deformations dominate the signal and not the mass changes. These examples illustrate that terrestrial clock networks can be used in the future as a modern tool for detecting various time-variable gravity signals for understanding the regional/local patterns of the variations and for providing complementary information to other geodetic techniques.

Gravity Observations and Apparent Density Changes before the 2017 Jiuzhaigou Ms7.0 Earthquake and Their Precursory Significance.

An Ms7.0 earthquake struck Jiuzhaigou (China) on 8 August 2017. The epicenter was in the eastern margin of the Tibetan Plateau, an area covered by a dense time-varying gravity observation network. Data from seven repeated high-precision hybrid gravity surveys (2014–2017) allowed the microGal-level time-varying gravity signal to be obtained at a resolution better than 75 km using the modified Bayesian gravity adjustment method. The “equivalent source” model inversion method in spherical coordinates was adopted to obtain the near-crust apparent density variations before the earthquake. A major gravity change occurred from the southwest to the northeast of the eastern Tibetan Plateau approximately 2 years before the earthquake, and a substantial gravity gradient zone was consistent with the tectonic trend that gradually appeared within the focal area of the Jiuzhaigou earthquake during 2015–2016. Factors that might cause such regional gravitational changes (e.g., vertical crustal deformation and variations in near-surface water distributions) were studied. The results suggest that gravity effects contributed by these known factors were insufficient to produce gravity changes as big as those observed, which might be related to the process of fluid material redistribution in the crust. Regional change of the gravity field has precursory significance for high-risk earthquake areas and it could be used as a candidate precursor for annual medium-term earthquake prediction.

Temporal Gravity Signals in Reprocessed GOCE Gravitational Gradients

The reprocessing of the satellite gravitational gradiometry (SGG) data from the Gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite mission in 2018/2019 considerably reduced the low-frequency noise in the data, leading to reduced noise amplitudes in derived gravity field models at large spatial scales, at which temporal variations of the Earth’s gravity field have their highest amplitudes. This is the motivation to test the reprocessed GOCE SGG data for their ability to resolve time-variable gravity signals. For the gravity field processing, we apply and compare a spherical harmonics (SH) approach and a mass concentration (mascon) approach. Although their global signal-to-noise ratio is <1, SH GOCE SGG-only models resolve the strong regional signals of glacier melting in Greenland and Antarctica, and the 2011 moment magnitude 9.0 earthquake in Japan, providing an estimation of gravity variations independent of Gravity Recovery and Climate Experiment (GRACE) data. The benefit of combined GRACE/GOCE SGG models is evaluated based on the ice mass trend signals in Greenland and Antarctica. While no signal contribution from GOCE SGG data additional to the GRACE models could be observed, we show that the incorporation of GOCE SGG data numerically stabilizes the related normal equation systems.

Variation of Antarctic circumpolar current and its intensification in relation to the southern annular mode detected in the time-variable gravity signals by GRACE satellite

The southern annular mode (SAM) in the atmosphere and the Antarctic circumpolar current (ACC) in the ocean play decisive roles in the climatic system of the mid- to high-latitude southern hemisphere. Using the time-variable gravity data from the GRACE satellite mission, we find the link between the space–time variabilities of the ACC and the SAM. We calculate the empirical orthogonal functions (EOF) of the non-seasonal ocean bottom pressure (OBP) field in the circum-Antarctic seas from the GRACE data for the period from 2003 to 2015. We find that the leading EOF mode of the non-seasonal OBP represents a unison OBP oscillation around Antarctica with time history closely in pace with that of the SAM Index with a high correlation of 0.77. This OBP variation gives rise to a variation in the geostrophic flow field; the result for the same EOF mode shows heightened variations in the zonal velocity that resides primarily in the eastern hemispheric portion of the ACC and coincided geographically with the southernmost boundary of the ACC’s main stream. Confirming previous oceanographic studies, these geodetic satellite results provide independent information toward better understanding of the ACC–SAM process.Graphical .

Gravity field models derived from Swarm GPS data

It is of great interest to numerous geophysical studies that the time series of global gravity field models derived from Gravity Recovery and Climate Experiment (GRACE) data remains uninterrupted after the end of this mission. With this in mind, some institutes have been spending efforts to estimate gravity field models from alternative sources of gravimetric data. This study focuses on the gravity field solutions estimated from Swarm global positioning system (GPS) data, produced by the Astronomical Institute of the University of Bern, the Astronomical Institute (ASU, Czech Academy of Sciences) and Institute of Geodesy (IfG, Graz University of Technology). The three sets of solutions are based on different approaches, namely the celestial mechanics approach, the acceleration approach and the short-arc approach, respectively. We derive the maximum spatial resolution of the time-varying gravity signal in the Swarm gravity field models to be degree 12, in comparison with the more accurate models obtained from K-band ranging data of GRACE. We demonstrate that the combination of the GPS-driven models produced with the three different approaches improves the accuracy in all analysed monthly solutions, with respect to any of them. In other words, the combined gravity field model consistently benefits from the individual strengths of each separate solution. The improved accuracy of the combined model is expected to bring benefits to the geophysical studies during the period when no dedicated gravimetric mission is operational.

Mass Flux Solution in the Tibetan Plateau Using Mascon Modeling

Mascon modeling is used in this paper to produce the mass flux solutions in the Tibetan Plateau. In the mascon modeling, the pseudo observations and their covariance matrices are derived from the GRACE monthly gravity field models. The sampling density of the pseudo observations is determined based on the eigenvalues of the covariance matrices. In the Tibetan Plateau, the sampling density of per 1.5° is the most appropriate among all choices. The mass flux variations from 2003 to 2014 are presented in this paper, which show large mass loss (about −15.5 Gt/year) in Tianshan, North India, and Eastern Himalaya, as well as strong positive signals (about 9 Gt/year) in the Inner Tibetan Plateau. After the glacier isostatic adjustment effects from Pau-5-AUT model are removed, the mass change rates in the Tibetan Plateau derived from CSR RL05, JPL RL05, GFZ RL05a, and Tongji-GRACE02 monthly models are −6.41 ± 4.74 Gt/year, −5.87 ± 4.88 Gt/year, −6.08 ± 4.65 Gt/year, and −11.50 ± 4.79 Gt/year, respectively, which indicate slight mass loss in this area. Our results confirm that mascon modeling is efficient in the recovery of time-variable gravity signals in the Tibetan Plateau.

Time-variable gravity fields derived from GPS tracking of Swarm

Abstract Since 2002 Gravity Recovery and Climate Experiment (GRACE) provides monthly gravity fields from K-band ranging (KBR) between two GRACE satellites. These KBR gravity monthlies have enabled the global observation of time-varying Earth mass signal at a regional scale (about 400 km resolution). Apart from KBR, monthly gravity solutions can be computed from onboard GPS data. The newly reprocessed GPS monthlies from 13 yr of GRACE data are shown to yield correct time-variable gravity signal (seasonality, trends, interannual variations) at a spatial resolution of 1300 km (harmonic degree 15). We show that GPS fields from new Swarm mission are of similar quality as GRACE GPS monthlies. Thus, Swarm GPS monthlies represent new and independent source of information on time-variable gravity, and, although with lower resolution and accuracy, they can be used for its monitoring, particularly if GRACE KBR/GPS data become unavailable before GRACE Follow-On is launched (2017 August).

Sensitivity of Goce Gradients on Greenland Mass Variation And Changes in Ice Topography

Abstract The Gravity field and steady state Ocean Circulation Explorer (GOCE) maps variations in the gravity field by observing second order derivatives (gradients) of the Earth gravitational potential. Flying in the low altitude of 255 km and having a spatially dense data distribution of short wavelengths of the gravity field, GOCE may be used to enhance the time varying gravity signal coming fromthe GRACE satellites. The GOCE gradients may potentially be used for the determination of residual masses in local regions. This can be done using Least-Squares Collocation (LSC) or the Reduced Point Mass (RPM) method. In this study, different gravity field solutions are calculated by the use of RPM, LSC and GOCE gradients, respectively. Gravity field time series are created and presented for the six consecutive months of GOCE gradient observations, data being acquired between November 2009 and June 2010. Corresponding gravity anomaly results are used for the calculation of ice mass changes by the use of theRPMmethod. The results are then compared with the computed topographic effect of the ice by the use of a modified topographic correction and the Gravsoft TC program. The maximal gravity changes at the ground predicted from GOCE gradients are between 2 and 4 mGal for the period considered. The gravity anomaly estimation error arising from the GOCE gradient data using only Tzz with an associated error of 20 mE is 11 mGal. This analysis shows the potential of using GOCE data for observations of ice mass changes although the GOCE dataset is limited to only six months. We expect four years of GOCE gradient observations to be available by mid-2014. This will increase the accuracy and spatial resolution of the GOCE measurements, which may lead to an accuracy necessary for observing ice mass changes.

Gravity field models from kinematic orbits of CHAMP, GRACE and GOCE satellites

The aim of our work is to generate Earth’s gravity field models from GPS positions of low Earth orbiters. Our inversion method is based on Newton’s second law, which relates the observed acceleration of the satellite with forces acting on it. The observed acceleration is obtained as numerical second derivative of kinematic positions. Observation equations are formulated using the gradient of the spherical harmonic expansion of the geopotential. Other forces are either modelled (lunisolar perturbations, tides) or provided by onboard measurements (nongravitational perturbations). From this linear regression model the geopotential harmonic coefficients are obtained.To this basic scheme of the acceleration approach we added some original elements, which may be useful in other inversion techniques as well. We tried to develop simple, straightforward and still statistically correct model of observations. (i) The model is linear in the harmonic coefficients, no a priori gravity field model is needed, no regularization is applied. (ii) We use the generalized least squares to successfully mitigate the strong amplification of noise due to numerical second derivative. (iii) The number of other fitted parameters is very small, in fact we use only daily biases, thus we can monitor their behaviour. (iv) GPS positions have correlated errors. The sample autocorrelation function and especially the partial autocorrelation function indicate suitability of an autoregressive model to represent the correlation structure. The decorrelation of residuals improved the accuracy of harmonic coefficients by a factor of 2–3. (v) We found it better to compute separate solutions in the three local reference frame directions than to compute them together at the same time; having obtained separate solutions for along-track, cross-track and radial components, we combine them using the normal matrices. Relative contribution of the along-track component to the combined solution is 50 percent on average. (vi) The computations were performed on an ordinary PC up to maximum degree and order 120.We applied the presented method to orbits of CHAMP and GRACE spanning seven years (2003–2009) and to two months of GOCE (Nov/Dec 2009). The obtained long-term static gravity field models are of similar or better quality compared to other published solutions. We also tried to extract the time-variable gravity signal from CHAMP and GRACE orbits. The acquired average annual signal shows clearly the continental areas with important and known hydrological variations.

Computations of the viscoelastic response of a 3-D compressible Earth to surface loading: an application to Glacial Isostatic Adjustment in Antarctica and Canada

S U M M A R Y We develop a 3-D finite-element model to study the viscoelastic response of a compressible Earth to surface loads. The effects of centre of mass motion, polar wander feedback, and selfconsistent ocean loading are implemented. To assess the model’s accuracy, we benchmark the numerical results against a semi-analytic solution for spherically symmetric structure.We force our model with the ICE-5G global ice loading history to study the effects of laterally varying viscosity structure on several glacial isostatic adjustment (GIA) observables, including relative sea-level (RSL)measurements inCanada, and present-day time-variable gravity and uplift rates in Antarctica. Canadian RSL observations have been used to determine the Earth’s globally averaged viscosity profile. AntarcticGPS uplift rates have been used to constrainAntarcticGIA models. And GIA time-variable gravity and uplift signals are error sources for GRACE and altimeter estimates of present-day Antarctic ice mass loss, and must be modelled and removed from those estimates. ComputingGIA results for a 3-D viscosity profile derived from a realistic seismic tomography model, and comparing with results computed for 1-D averages of that 3-D profile, we conclude that: (1) a GIA viscosity model based on Canadian relative sea-level data is more likely to represent a Canadian average than a true global average; (2) the effects of 3-D viscosity structure on GRACE estimates of present-day Antarctic mass loss are probably smaller than the difference between GIA models based on different Antarctic deglaciation histories and (3) the effects of 3-D viscosity structure on Antarctic GPS observations of present-day uplift rate can be significant, and can complicate efforts to use GPS observations to constrain 1-D GIA models.

On the impact of topography and building mask on time varying gravity due to local hydrology

We use 3 yr of surface absolute gravity measurements at three sites on the Larzac plateau (France) to quantify the changes induced by topography and the building on gravity time-series, with respect to an idealized infinite slab approximation. Indeed, local topography and buildings housing ground-based gravity measurement have an effect on the distribution of water storage changes, therefore affecting the associated gravity signal. We first calculate the effects of surrounding topography and building dimensions on the gravity attraction for a uniform layer of water. We show that a gravimetric interpretation of water storage change using an infinite slab, the so-called Bouguer approximation, is generally not suitable. We propose to split the time varying gravity signal in two parts (1) a surface component including topographic and building effects (2) a deep component associated to underground water transfer. A reservoir modelling scheme is herein presented to remove the local site effects and to invert for the effective hydrological properties of the unsaturated zone. We show that effective time constants associated to water transfer vary greatly from site to site. We propose that our modelling scheme can be used to correct for the local site effects on gravity at any site presenting a departure from a flat topography. Depending on sites, the corrected signal can exceed measured values by 5-15 μGal, corresponding to 120-380 mm of water using the Bouguer slab formula. Our approach only requires the knowledge of daily precipitation corrected for evapotranspiration. Therefore, it can be a useful tool to correct any kind of gravimetric time-series data.

Separation of mass signals within GRACE monthly gravity field models by means of empirical orthogonal functions

Since 2002 the two GRACE satellites observe the time varying gravity signal mainly caused by the sum of mass variations within the Earth subsystems ocean, atmosphere, and continental hydrosphere. It is a challenging problem to separate the integral GRACE signal and to identify and quantify the mass variations of the individual subsystems. This work proves first by a closed loop simulation that such a decomposition is successful by means of empirical orthogonal functions (EOF) derived from geophysical models and a least-squares adjustment with a multivariate Gauss–Markov model with time coefficients parameterized. The geophysical models are used to synthesize GRACE observations which are subsequently separated leading to time coefficients coinciding with those of the predefined models. In a second step the separation is performed with real, unfiltered time series of 5years of monthly GRACE gravity field models (with atmospheric and oceanic background models reconstructed) and a limited number of EOFs. The reconstructed time coefficients are in good agreement with the original ones and exhibit high correlations (0.70 for ocean, 0.91 for atmosphere and 0.93 for continental hydrosphere). Analysis of GRACE residuals and the correlation among the time coefficients substantiate a successful identification.

Separation of global time-variable gravity signals into maximally independent components

The Gravity Recovery and Climate Experiment (GRACE) products provide valuable information about total water storage variations over the whole globe. Since GRACE detects mass variations integrated over vertical columns, it is desirable to separate its total water storage anomalies into their original sources. Among the statistical approaches, the principal component analysis (PCA) method and its extensions have been frequently proposed to decompose the GRACE products into space and time components. However, these methods only search for decorrelated components that on the one hand are not always interpretable and on the other hand often contain a superposition of independent source signals. In contrast, independent component analysis (ICA) represents a technique that separates components based on assumed statistical independence using higher-order statistical information. If one assumes that independent physical processes generate statistically independent signal components added up in the GRACE observations, separating them by ICA is a reliable strategy to identify these processes. In this paper, the performance of the conventional PCA, its rotated extension and ICA are investigated when applied to the GRACE-derived total water storage variations. These analyses have been tested on both a synthetic example and on the real GRACE level-2 monthly solutions derived from GeoForschungsZentrum Potsdam (GFZ RL04) and Bonn University (ITG2010). Within the synthetic example, we can show how imposing statistical independence in the framework of ICA improves the extraction of the ‘original’ signals from a GRACE-type super-position. We are therefore confident that also for the real case the ICA algorithm, without making prior assumptions about the long-term behaviour or on the frequencies contained in the signal, improves over the performance of PCA and its rotated extension in the separation of periodical and long-term components.

Tackling mass redistribution phenomena by time-dependent GRACE- and terrestrial gravity observations

Time variable gravity field models derived from the satellite mission GRACE have been demonstrated to be consistent with water mass variations in the global hydrological cycle. Independent observations are provided by terrestrial measurements. In order to achieve a maximum of reliability and information gain, ground-based gravity observations may be deployed for comparison with the gravity field variations derived from the GRACE satellite mission. In this context, the data of the network of superconducting gravimeters (SG) of the ‘Global Geodynamics Project’ (GGP) are of particular interest. This study is focused on the dense SG network in Central Europe with its long-term gravity observations. It is shown that after the separation and reduction of local hydrological effects in the SG observations especially for subsurface stations, the time-variable gravity signals from GRACE agree well with the terrestrial observations from the SG station cluster.Station stability of the SG sites with respect to vertical deformations was checked by GNSS based observations. Most of the variability can be explained by loading effects due to changes in continental water storage, and, in general, the stability of all stations has been confirmed.From comparisons based on correlation and coherence analyses in combination with the root mean square (RMS) variability of the time series emerges, that the maximum correspondence between the SG and GRACE time series is achieved when filtering the GRACE data with Gaussian filters of about 1000km filter length, which is in accordance with previous publications.Empirical Orthogonal Functions (EOF) analysis was applied to the gravity time series in order to identify common characteristic spatial and temporal patterns. The high correspondence of the first modes for GRACE and SG data implies that the first EOF mode represents a large-scale (Central European) time-variable gravity signal seen by both the GRACE satellites and the SG cluster.

Supporting large-scale hydrogeological monitoring and modelling by time-variable gravity data

One of the key variables for hydrogeological monitoring and modelling studies are temporal variations of the water storage in groundwater systems. Storage variations are a fundamental component of the groundwater balance and of the continental water cycle. Assessing groundwater storage change is central to water management with regard to water resources, ecology (e.g., wetland preservation), or engineering (e.g., land subsidence due to groundwater withdrawal). In particular, at large spatial scales, however, measuring groundwater storage change is demanding. A dense observation network of wells is required to obtain reliable area-average values. For large aquifers or river basins and for remote areas this approach may not be feasible. An alternative method that has rarely been applied in hydrogeology is to monitor mass changes that are associated with water storage variations by means of gravity surveys. Measurements with superconducting gravimeters have shown that variations in the groundwater level or other hydrological features near the monitoring station may have a significant impact on the observed time-variable gravity signal (Kroner and Jahr 2006) and allow for the determination of aquifer storage changes at the local scale (Pool 2005). At the global scale, gravity satellite missions make use of the basic principle that the satellite’s motion around the Earth is dominated by the Earth’s gravity field. Thus, tracking perturbations of the satellite orbit allows for the determination of the underlying spatial and temporal variations of the gravity field. These very small perturbations originate, on the one hand, from the spatially inhomogeneous but quasi-static mass distribution of the solid Earth and, on the other hand, from the even smaller temporal variations caused by mass fluxes in the vicinity of the Earth’s surface by the atmosphere, oceans, and hydrosphere. The breakthrough with respect to hydrological applications, as outlined in the fundamental contributions byWahr et al. (1998) and Dickey et al. (1999), came with the GRACE. The GRACE (Gravity Recovery and Climate Experiment) satellite mission was launched by NASA (National Aeronautics and Space Administration) and DLR (German Aerospace Center) in March 2002 (Tapley et al. 2004a). The objective of GRACE is to map the Earth’s gravity field every month with a spatial resolution of a few hundred km. The mission consists of two identical satellites co-orbiting in the same, almost polar, orbital plane at a distance of approximately 220 km from each other along their track and at an initial altitude of about 500 km. The key element is a micrometer precise satellite link continuously measuring the relative distance of the satellites from each other, which is highly sensitive to the variations of the gravity field. While the intended lifetime of GRACE was 5 years, a considerably longer lifetime reaching until about 2016 can be expected based on the actual mission status (according to an internal report by GFZ Potsdam (Germany’s National Research Centre for Geosciences, 2006, unpublished data). Recent results mark impressive progress in the determination of mass variations relevant for large-scale hydrological and hydrogeological purposes from GRACE time-variable gravity fields. Several studies inferred water storage change on the continents (e.g., Andersen et al. 2005; Ramillien et al. 2005; Schmidt et al. 2006b; Swenson and Milly 2006; Tapley et al. 2004b; Wahr et al. 2004). The results clearly show seasonal and inter-annual changes in water storage at the scale of continents and large river basins that roughly correspond to simulation results of global hydrological and climate models. Discrepancies in amplitude and phase help to identify model limitations such as in model structure, process description or parameterization, and errors in model input data such as climate data. On the other hand, several error components are added to GRACE data during progression from raw satellite data to the final hydrological product: (1) GRACE measurement errors (such as instrument or orbit errors), (2) errors in the background data used for reducing the total GRACE mass signal to (ground) water variations, and (3) leakage of signals from outside a selected region of interest.

On inversion for mass distribution from global (time-variable) gravity field

The well-known non-uniqueness of the gravitational inverse problem states that the external gravity field, even if completely and exactly known, cannot uniquely determine the density distribution of the body that produces the gravity field. In this paper, we provide conceptual insight by examining the problem in terms of spherical harmonic expansion of the global gravity field. By comparing the multipoles and the moments of the density function, we show that in 3-D the degree of knowledge deficiency in trying to inversely recover the density distribution from an external gravity field solution is (n + 1)(n + 2)/2 − (2n + 1) = n(n − 1)/2 for each harmonic degree n. On the other hand, on a 2-D spherical shell we show via a simple relationship that the inverse solution of the surface density distribution is unique. The latter applies quite readily in the inversion of time-variable gravity signals (such as those observed by the GRACE space mission) where the sources largely come from the Earth's surface over a wide range of timescales.

Can tectonic processes be recovered from new gravity satellite data?

The goal of this study is to investigate whether temporal variations of the gravity field caused by tectonic processes (hereafter geodynamic signals) can be recognized in satellite gravity data, including the currently operating GRACE satellites and future systems. We restricted our study to subduction zones, calculating possible gravity field variations associated with elastic stress accumulation in locked areas and with stress release by earthquakes. We used fault-plane solutions for the Alaska-1964, Chile-1960 and Hokkaido-2003 earthquakes, and GPS-based strain accumulation data in locked areas of the Alaska subduction zone. Vertical displacements of the Earth's surface were calculated using a model of a rectangular fault in an elastic half-space. We developed and applied a statistical signal-recognition technique to identify signals caused by displacements of unknown magnitude on fault planes of given position and dimension. Our goal is thus to detect and analyse in satellite gravity data a signal constrained by ground geophysical and geodetic data. We assumed different levels of data accuracy, ranging from the first GRACE model GGM-01S to two orders of magnitude lower, corresponding to the target accuracy for GRACE and GOCE data. We concluded that using the developed technique, gravity field variations similar to those caused by Alaska-1964 earthquake should be recognizable in GRACE data at the accuracy level of the model GGM-01S. If forthcoming satellite gravity models have an accuracy one order of magnitude better, then the signal recognition probability will be about 99% using our approach. The required accuracy is close to the errors due to imperfect corrections for atmospheric effects. For the Chile-1960 earthquake we considered different fault-plane models and found that one can distinguish between these models with a probability approaching 70% at present level of GRACE accuracy. Increasing the data accuracy by one order of magnitude makes this probability very high. Because the gravity signal from the Hokkaido-2003 earthquake was rather weak, it would only be recognized if the data accuracy increases by two orders of magnitude thus approaching the target GRACE accuracy. If forthcoming gravity models are one order of magnitude more accurate compared to the first GRACE model then 5 years of data will allow recognition of time varying gravity signal associated with locked areas of the Alaska subduction zone. Our method may be easily applied to other geodynamic targets and more generally be adapted to other time varying gravity studies.