Joint estimation of gravity anomalies using second and third order potential derivatives

Author(s):  
Mohsen Romeshkani ◽  
Mohammad A Sharifi ◽  
Dimitrios Tsoulis

Abstract Satellite gradiometry data provide the framework for estimating and validating Earth's gravity field from second and third order derivatives of the Earth's gravitational potential. Such procedures are especially useful when applied locally, as they relate to local and regional characteristics of the real gravity field. In the present study a joint inversion procedure is proposed for the estimation of gravity anomalies at sea surface level from second and third order potential derivatives, based on a standard Gauss-Markov estimation model. The estimation procedure is applied for a test area stretching over Iran involving simulated grids from GOCE-only model GGM_TIM_R05 at GOCE altitude and gravity anomalies recovered at sea level. In order to validate the proposed estimation three different reductions have been considered independently, namely the removal of the long-wavelength part of the observed field through a global gravity model, the removal of the high-frequency part of the field through the incorporation of a topographic/isostatic gravity model and the application of variance component estimation. The application of a global gravity model leads to an improvement in the individual component estimation of the order of magnitude 3 per cent to 73 per cent, with a significant reduction in bias to 4 mGal. Smoother gradient components can come out according to removing the topography and taking into account for isostasy that improved up results of recovery to 25 per cent for the radial second order derivative. Finally, the implementation of variance component estimation leads to no significant improvement in results of recovered gravity anomalies.

2003 ◽  
Vol 1 ◽  
pp. 81-85 ◽  
Author(s):  
J. Kusche

Abstract. In the course of level 2 data processing for the GOCE (Gravity Field and Steady–State Ocean Circulation Explorer) satellite mission different streams of level 1b data will be merged in a single solution providing the Earth’s gravity field, but also state-vector parameters and other quantities. A proper weighting of orbit tracking data, gravity gradiometry data and certain a priori information, usually considered as ‘solution constraints’, can be expected as crucial for the solution quality. But the a priori stochastic models, based on pre–mission assessment of the expected instrument behaviour, may be over–optimistic or even too pessimistic since they refer to an unprecedented mission with scientific payload never tested in space. One way to derive an optimal weighting scheme on a statistically sound basis while simultaneously validating the stochastic noise levels of the data is by including variance component estimation as a part of the level 1b to level 2 data analysis process. The idea is that by applying Monte-Carlo techniques this method can be extended to a large-scale problem like GOCE data analysis, using software modules that already exist or are currently under development. The proposed method has been tested using simulated GOCE orbit trajectories as well as gravity gradiometry data corrupted by colored random noise.Key words. GOCE, gravity field modelling, combination solutions, weight estimation, variance component estimation


2021 ◽  
pp. 1-16
Author(s):  
Hong Hu ◽  
Xuefeng Xie ◽  
Jingxiang Gao ◽  
Shuanggen Jin ◽  
Peng Jiang

Abstract Stochastic models are essential for precise navigation and positioning of the global navigation satellite system (GNSS). A stochastic model can influence the resolution of ambiguity, which is a key step in GNSS positioning. Most of the existing multi-GNSS stochastic models are based on the GPS empirical model, while differences in the precision of observations among different systems are not considered. In this paper, three refined stochastic models, namely the variance components between systems (RSM1), the variances of different types of observations (RSM2) and the variances of observations for each satellite (RSM3) are proposed based on the least-squares variance component estimation (LS-VCE). Zero-baseline and short-baseline GNSS experimental data were used to verify the proposed three refined stochastic models. The results show that, compared with the traditional elevation-dependent model (EDM), though the proposed models do not significantly improve the ambiguity resolution success rate, the positioning precision of the three proposed models has been improved. RSM3, which is more realistic for the data itself, performs the best, and the precision at elevation mask angles 20°, 30°, 40°, 50° can be improved by 4⋅6%, 7⋅6%, 13⋅2%, 73⋅0% for L1-B1-E1 and 1⋅1%, 4⋅8%, 16⋅3%, 64⋅5% for L2-B2-E5a, respectively.


2021 ◽  
Author(s):  
Saniya Behzadpour ◽  
Andreas Kvas ◽  
Torsten Mayer-Gürr

<p>Besides a K-Band Ranging System (KBR), GRACE-FO carries a Laser Ranging Interferometer (LRI) as a technology demonstration to provide measurements of inter-satellite range changes. This additional measurement technology provides supplementary observations, which allow for cross-instrument diagnostics with the KBR system and, to some extent, the separation of ranging noise from other sources such as noise in the on-board accelerometer (ACC) measurements.</p><p>The aim of this study is to incorporate the two ranging systems (LRI and KBR) observations in ITSG-Grace2018 gravity field recovery. The two observation groups are combined in an iterative least-squares adjustment with variance component estimation used to determine the unknown noise covariance functions for KBR, LRI, and ACC measurements. We further compare the gravity field solutions obtained from the combined solutions to KBR-only results and discuss the differences with a focus on the global gravity field and LRI calibration parameters.</p>


Metrika ◽  
1995 ◽  
Vol 42 (1) ◽  
pp. 215-230 ◽  
Author(s):  
Shayle R. Searle

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