Improved pre-processing algorithm for satellite gravimetry data using wavelet method

Abstract

As for the ultra high performance to determine unique earth gravity field model and its geoid, systematic errors and existing outliers need to be removed from the satellite gravimetry observation before scientific product process. In this work, we introduced an improved pre-processing algorithm for satellite gravimetry data. Firstly, scale-factors of observations are calibrated based on certain regional terrestrial-gravity data. Then on the basis of wavelet theory, an outlier-detection algorithm for satellite gravity gradiometry by applying a wavelet de-noising method to some simulation data with white noise and outliers is proposed. The computation result shows that this novel algorithm has a 97 % success rate in outlier identification and that it can be efficiently used for pre-processing real Satellite Gravity Gradiometry data.