Translation-invariant wavelet denoising of full-tensor gravity –gradiometer data
Zhang Dai-Lei1, Huang Da-Nian1, Yu Ping1, and Yuan Yuan2,3
1. College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China.
2. The Second Institute of Oceanography, the State Oceanic Administration, Hangzhou 310012, China.
3. Key Laboratory of Submarine Geoscience, the State Oceanic Administration, Hangzhou 310012, China.
Abstract：
Denoising of full-tensor gravity-gradiometer data involves detailed information from field sources, especially the data mixed with high-frequency random noise. We present a denoising method based on the translation-invariant wavelet with mixed thresholding and adaptive threshold to remove the random noise and retain the data details. The novel mixed thresholding approach is devised to filter the random noise based on the energy distribution of the wavelet coefficients corresponding to the signal and random noise. The translation-invariant wavelet suppresses pseudo-Gibbs phenomena, and the mixed thresholding better separates the wavelet coefficients than traditional thresholding. Adaptive Bayesian threshold is used to process the wavelet coefficients according to the specific characteristics of the wavelet coefficients at each decomposition scale. A two-dimensional discrete wavelet transform is used to denoise gridded data for better computational efficiency. The results of denoising model and real data suggest that compared with Gaussian regional filter, the proposed method suppresses the white Gaussian noise and preserves the high-frequency information in gravity-gradiometer data. Satisfactory denoising is achieved with the translation-invariant wavelet.
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2017, Vol. 14 
