Translation-invariant wavelet denoising of full-tensor gravity-gradiometer data  

基于平移不变量小波的全张量重力梯度数据滤波（英文）

作　　者：Zhang Dai-Lei Huang Da-Nian Yu Ping Yuan Yuan 张代磊;黄大年;于平;袁园(吉林大学地球探测科学与技术学院,长春130026;国家海洋局第二海洋研究所,杭州310012;国际海洋局海底科学重点实验室,杭州310012)

机构地区：[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.

出　　处：《Applied Geophysics》2017年第4期606-619,623,共15页应用地球物理（英文版）

基　　金：supported by the National Key Research and Development Plan Issue(Nos.2017YFC0602203 and2017YFC0601606);the National Science and Technology Major Project Task(No.2016ZX05027-002-003);the National Natural Science Foundation of China(Nos.41604089 and 41404089);the State Key Program of National Natural Science of China(No.41430322);the Marine/Airborne Gravimeter Research Project(No.2011YQ12004505);the State Key Laboratory of Marine Geology,Tongji University(No.MGK1610);the Basic Scientific Research Business Special Fund Project of Second Institute of Oceanography,State Oceanic Administration(No.14275-10)

摘　　要：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.全张量重力梯度(FTG)数据包含大量场源体的细节信息,其滤波处理对异常的反演和解释结果有重要影响,本文提出一种基于平移不变量小波的自适应混合阈值滤波方法,可有效压制随机噪声并保留数据细节信息。建立了新的混合阈值法,根据信号和随机噪声所对应小波系数的能量分布进行滤波。平移不变量小波能有效压制伪吉布斯现象,混合阈值方法相对传统阈值能得到更好的信噪小波系数的分离,同时,根据每个分解尺度上小波系数的统计特性,使用自适应贝叶斯阈值进行小波系数的处理。此外,应用二维离散小波变换直接处理网格数据,可以提高计算效率。模型数据和实测数据处理的结果表明,相对高斯滤波器,本文所提出的方法不仅能有效去除高斯白噪声,还能更好地保留FTG数据的高频细节信息,具有良好的实际应用前景。

关 键 词：TENSOR gravity gradiometry DENOISING threshold translation-invariant wavelet 

分 类 号：P631.1[天文地球—地质矿产勘探]