Abstract
Gravity data gaps in mountainous areas are nowadays often filled in with the data from airborne gravity surveys. Because of the errors caused by the airborne gravimeter sensors, and because of rough flight conditions, such errors cannot be completely eliminated. The precision of the gravity disturbances generated by the airborne gravimetry is around 3–5 mgal. A major obstacle in using airborne gravimetry are the errors caused by the downward continuation. In order to improve the results the external high-accuracy gravity information e.g., from the surface data can be used for high frequency correction, while satellite information can be applying for low frequency correction. Surface data may be used to reduce the systematic errors, while regularization methods can reduce the random errors in downward continuation. Airborne gravity surveys are sometimes conducted in mountainous areas and the most extreme area of the world for this type of survey is the Tibetan Plateau. Since there are no high-accuracy surface gravity data available for this area, the above error minimization method involving the external gravity data cannot be used. We propose a semi-parametric downward continuation method in combination with regularization to suppress the systematic error effect and the random error effect in the Tibetan Plateau; i.e., without the use of the external high-accuracy gravity data. We use a Louisiana airborne gravity dataset from the USA National Oceanic and Atmospheric Administration (NOAA) to demonstrate that the new method works effectively. Furthermore, and for the Tibetan Plateau we show that the numerical experiment is also successfully conducted using the synthetic Earth Gravitational Model 2008 (EGM08)-derived gravity data contaminated with the synthetic errors. The estimated systematic errors generated by the method are close to the simulated values. In addition, we study the relationship between the downward continuation altitudes and the error effect. The analysis results show that the proposed semi-parametric method combined with regularization is efficient to address such modelling problems.
Highlights
Airborne gravimetry is a cost efficient way of collecting gravity data in mountainous and uninhabited areas [1,2,3,4,5,6]
We focus on the following issues: (1) the effectiveness of the semi-parametric model combined with the regularization in removing the errors on the Louisiana-project data; (2) the effectiveness of the semi-parametric method in modeling the systematic errors in the Tibetan plateau; (3) the relationship between the errors and the flight altitude in downward continuation (11 km for the Louisiana project as well as for the future Tibetan Plateau project)
To confirm that the downward continuation errors of our experiment for the Tibetan Plateau experiment is caused by the severe terrain roughness, we show the topography profiles in this area (Figure 6)
Summary
Airborne gravimetry is a cost efficient way of collecting gravity data in mountainous and uninhabited areas [1,2,3,4,5,6]. The downward continuation of airborne gravity data from the flight altitude to the surface is frequently used in the geophysical/geodetic applications, i.e., contemporary geoid models [3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]. The downward continuation that inverts the solution of Abel-Poisson’s integral, has become a standard strategy applied by many authors. The problem was treated using Tikhonov’s regularization [21]
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