Method Based on Wavelet and Empirical Mode Decomposition for Extracting the Gravity Signal

Abstract:

The measurement data of the marine gravity contains a lot of noise, the low frequency part of which have a similar frequency with the gravity signal. It’s difficult to inhibit the noise of the measurement data and extract the gravity signal by classical algorithms. Therefore, in order to effectively eliminate the noise of the measurement gravity data and improve the accuracy of the extracted signal, based on algorithms of wavelet and Empirical Mode Decomposition (EMD), a novel method to extract the sea gravity anomaly signal is proposed. Firstly, the measurement gravity signal is decomposed into detail signals and approximate signals. Secondly, the algorithm EMD is used to extract the low-frequency part of the decomposed signals, and the estimation of the gravity anomaly is reconstructed by inverse wavelet transform. The de-noising experiment has been emulated based on the measurement gravity data. Results of theoretical analysis and emulation experiments indicate that the proposed method can effectively eliminate the noise of the measurement gravity and recovery the wave form of gravity signal, the accuracy of the signal can be approximately increased 40% than classical algorithms.

• Periodical: Applied Mechanics and Materials (Volumes 668-669)
• Pages: 1076-1080
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.668-669.1076
• Online since: October 2014
• Authors: Li Ye Zhao*
• Keywords: Kalman Filter, Mode Decomposition (EMD), Signal of Gravimeters, Wavelet Translation

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