Airborne gravity data denoising based on empirical mode decomposition: A case study for SGA-WZ Greenland test data

Abstract

Surveying the Earth's gravity field refers to an important domain of Geodesy, involving deep connections with Earth Sciences and Geo-information. Airborne gravimetry is an effective tool for collecting gravity data with mGal accuracy and a spatial resolution of several kilometers. The main obstacle of airborne gravimetry is extracting gravity disturbance from the extremely low signal to noise ratio measuring data. In general, the power of noise concentrates on the higher frequency of measuring data, and a low pass filter can be used to eliminate it. However, the noise could distribute in a broad range of frequency while low pass filter cannot deal with it in pass band of the low pass filter. In order to improve the accuracy of the airborne gravimetry, Empirical Mode Decomposition (EMD) is employed to denoise the measuring data of two primary repeated flights of the strapdown airborne gravimetry system SGA-WZ carried out in Greenland. Comparing to the solutions of using finite impulse response filter (FIR), the new results are improved by 40% and 10% of root mean square (RMS) of internal consistency and external accuracy, respectively.