With the increasing number of various types of high-resolution gravity observations, earth gravity models can be regionally refined. We use Abel-Poisson kernel to represent the gravity as the linear summation of finite radial basis functions and combine the multiple gravity data to build a regional gravity model with high resolution. The minimum root mean square criterion based on the data adaptive algorithm is proposed to calculate the base function, which promote the speed of computation significantly. Taking the central South China Sea as an example, two different types of gravity data, namely geoid undulations with resolution of 6'×6' and gravity anomaly with resolution of 2'×2', are used to construct the high-resolution regional gravity model. The model has a resolution of 2'×2', and has a great agreement with original gravity anomaly, reaching to ±0.8×10-5m/s2.Our results show that using radial basis functions to construct the regional gravity field can avoid the problem of slow convergence of spherical harmonic functions, and can improve the resolution remarkably.
MA Zhiwei
,
LU Yang
,
TU Yi
,
ZHU Chuandong
,
XI Hui
. Regional Gravity Field Modeling with Abel-Poisson Radial Basis Functions[J]. Acta Geodaetica et Cartographica Sinica, 2016
, 45(9)
: 1019
-1027
.
DOI: 10.11947/j.AGCS.2016.20150519
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