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

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.

Network traffic prediction method based on wavelet transform and multiple models fusion

“…The essence of Gauss‐Markov estimation is a special case of weighted least square estimation 59 . Its function is to make the error variance of fusion result smaller than that of single model prediction result.…”

“…The essence of Gauss-Markov estimation is a special case of weighted least square estimation. 59 Its function is to make the error variance of fusion result smaller than that of single model prediction result. The Gauss-Markov estimation algorithm is as the following: Supposing there are L prediction models.…”

Evaluation of gravitational curvatures for a tesseroid and spherical shell with arbitrary-order polynomial density

“…With the first measurement of radial gravitational curvatures (or gravity curvatures, GC, third-order derivatives of the gravitational potential) using the atom interferometry sensors in the laboratory condition (Rosi et al 2015), the subsequent study of the GC was focused on their theory and simulation applications in geodesy and geophysics (Šprlák and Novák 2015(Šprlák and Novák , 2017Hamáčková et al 2016;Sharifi et al 2017;Novák et al 2017;Pitoňák et al 2017Pitoňák et al , 2018Pitoňák et al , 2020Shen 2018a, b, 2019;Novák et al 2019;Du and Qiu 2019;Romeshkani et al 2020Romeshkani et al , 2021. The GC were more sensitive to field sources than the loworder gravitational effects (i.e., GP, GV, and GGT) (Heck 1984;Du and Qiu 2019).…”

“…The GC were more sensitive to field sources than the loworder gravitational effects (i.e., GP, GV, and GGT) (Heck 1984;Du and Qiu 2019). Numerical results showed that they were more sensitive to local characteristics of the gravity field (Romeshkani et al 2020), can better describe areas with large terrain undulations and the junction of the land and sea (Deng and Shen 2019), and can more accurately identify the near-underground shallow density structure in the spatial domain (e.g., caves, salt domes, sediment base morphology, continental margins, and hidden fault systems) (Novák et al 2019) than the low-order gravitational effects. These advantages of the GC are important for studying the distribution of materials and geological structures within the Earth.…”