基于曲波变换的航空电磁数据去噪方法研究

doi:10.6038/cjg2020N0365

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (9446 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要航空电磁法作为一种地形复杂地区资源探测的有效方法，近年来得到了广泛的应用.然而，由于系统所处的动态环境，噪声干扰严重.为了改善航空电磁数据质量，提高地下电性反演的准确性，需要研发相关去噪技术.传统航电去噪大多针对特定噪声或单一测线上的信号进行处理，难以兼顾相邻测线之间观测信号的相关性.本文采用曲波变换进行二维航空电磁数据去噪.由于曲波变换具有多尺度和多方向性特征，可以在对噪声精细分析的基础上进行有效去除，同时还保证了整个测区内信号的相关性.进而，我们提出Sigmoid阈值函数对传统阈值函数进行改进，以进一步改善去噪效果.为了验证曲波变换方法对航空电磁数据去噪的有效性，将曲波变换和传统去噪方法分别应用于理论模型和实测数据进行对比.试验证明本文曲波变换用于航空电磁数据去噪具有明显的优越性.

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	王宁
	殷长春
	高玲琦
	苏扬
	刘云鹤
	熊彬

关键词 ：航空电磁法, 曲波变换, 去噪, 多尺度分析

Abstract：The airborne electromagnetic (AEM) method has become an effective tool for exploration in areas with complex topography and is widely applied in the world. However, most current AEM systems are mounted or towed in a dynamic environment, resulting in big noise interference. To improve the quality of AEM data and interpretation, the denoising technique needs to be developed. Traditional denoising methods work mostly on special noise or single survey lines, without taking into account the correlation of signal at neighboring survey lines. In this work, we develop a denoising technique based on the curvelet transform for AEM data. As curvelet transform has the characteristics of multiple-scale and multiple-direction, it can remove the noise based on detailed analysis to the signal, while at the same time it considers the signal correlation between neighboring survey lines. Further, we improve the quality of denoising results by introducing the Sigmoid threshold function. We test our method by denoising both synthetic and survey data. The numerical results show that the curvelet-based method has obvious advantage in denoising AEM data.

Key words： Airborne EM Curvelet transform Denoising Multi-scale analysis

收稿日期: 2019-10-04

PACS:

P631

基金资助:国家自然科学基金项目（41774125，42030806，41530320，41904104，42074120）及北京市科技计划（Z181100005718001）联合资助.

通讯作者: 殷长春,男,1965年生,教授,国家特聘专家,主要从事电磁勘探理论,特别是航空和海洋电磁方面的研究.E-mail:yinchangchun@jlu.edu.cn E-mail: yinchangchun@jlu.edu.cn

作者简介: 王宁,女,1997年生,硕士研究生,主要从事航空电磁数据处理及正反演研究.E-mail:wangning1509@163.com

链接本文:

http://www.geophy.cn//CN/10.6038/cjg2020N0365 或 http://www.geophy.cn//CN/Y2020/V63/I12/4592

Abma R, Kabir N. 2006.3D interpolation of irregular data with a POCS algorithm. Geophysics, 71(5):E91-E97.
Candès E, Donoho D L. 1999. Curvelets:A Surprisingly Effective Nonadaptive Representation for Objects with Edges. Nashville:Vanderbilt University Press, 1-10.
Candès E, Donoho D L. 2004. New tight frames of curvelets and optimal representations of objects with piecewise C² singularities. Communications on Pure and Applied Mathematics, 57(2):219-266.
Candès E, Demanet L, Donoho D, et al. 2006. Fast discrete curvelet transforms. Multiscale Modeling & Simulation, 5(3):861-899.
Cao J J, Yang Z Q, Yang Y, et al. 2018. An adaptive seismic random noise elimination method based on curvelet transform. Geophysical Prospecting for Petroleum (in Chinese), 57(1):72-78, 121.
Donoho D L, Johnstone J M. 1994. Ideal spatial adaptation by wavelet shrinkage. Biometrika, 81(3):425-455.
Donoho D L. 1995. De-noising by soft-thresholding. IEEE Transactions on Information Theory, 41(3):613-627.
Du X Z, Lu C D. 2016-11-09. Singular value decomposition and wavelet analysis-based time-domain aeronautical electromagnetic data denoising method,CN,106094046A.
Herrmann F J, Hennenfent G. 2008. Non-parametric seismic data recovery with curvelet frames. Geophysical Journal International, 173(1):233-248.
Li J W, Liu X B, Zhou J H, et al. 2019. A curvelet threshold iteration method based on energy ratio for surface-wave suppression. Oil Geophysical Prospecting (in Chinese), 54(5):997-1004.
Li L F, Zhang X C, Zhang H, et al. 2015. Feature extraction of non-stochastic surfaces using curvelets. Precision Engineering, 39:212-219.
Qi S H, Liu Q Y, Chen J H, et al. 2016. Attenuation of noise in receiver functions using curvelet transform. Chinese Journal of Geophysics (in Chinese), 59(3):884-896, doi:10.6038/cjg20160311.
Reninger P A, Martelet G, Deparis J, et al. 2011. Singular value decomposition as a denoising tool for airborne time domain electromagnetic data. Journal of Applied Geophysics, 75(2):264-276.
Ridsdill-Smith T A, Dentith M C. 1999. The wavelet transform in aeromagnetic processing. Geophysics, 64(4):1003-1013.
Sykes M P, Das U C. 1998. Removal of herringbone effects from AEM data maps using the Radon transform. Exploration Geophysics, 29(1-2):92-95.
Tao K, Zhu J J. 2012. A comparative study on validity assessment of wavelet de-noising. Journal of Geodesy and Geodynamics (in Chinese), 32(2):128-133.
Wang L P. 2006. Plancherel theorem in L²(R_n). Journal of the Central University for Nationalities (Natural Sciences Edition) (in Chinse), 15(4):334-338.
Ying L X, Demanet L, Candès E. 2005.3D discrete curvelet transform.//Proceedings of SPIE 5914, Wavelets XI. San Diego, California, United States:SPIE, 351-361.
Zhang H L, Liu T Y, Zhang Y C. 2010. High order correlation based dip angle scanning noise elimination method in curvelet domain and space domain. Oil Geophysical Prospecting (in Chinese), 45(2):208-214.
Zhu K G, Wang L Q, Xie B, et al. 2013. Noise removal for airborne electromagnetic data based on principal component analysis. The Chinese Journal of Nonferrous Metals (in Chinese), 23(9):2430-2435.
附中文参考文献
曹静杰, 杨志权, 杨勇等. 2018. 一种基于曲波变换的自适应地震随机噪声消除方法. 石油物探, 57(1):72-78,121.
杜兴忠, 陆从德. 2016-11-09. 基于奇异值分解和小波分析的时间域航空电磁数据去噪方法. 中国, 106094046A.
李继伟, 刘晓兵, 周俊骅等. 2019. 基于能量比的Curvelet阈值迭代面波压制. 石油地球物理勘探, 54(5):997-1004.
齐少华, 刘启元, 陈九辉等. 2016. 接收函数的曲波变换去噪. 地球物理学报, 59(3):884-896, doi:10.6038/cjg20160311.
陶珂, 朱建军. 2012. 小波去噪质量评价方法的对比研究. 大地测量与地球动力学, 32(2):128-133.
王丽萍. 2006. 平方可测空间的Plancherel定理. 中央民族大学学报(自然科学版), 15(4):334-338.
张恒磊, 刘天佑, 张云翠. 2010. 基于高阶相关的Curvelet域和空间域的倾角扫描噪声压制方法. 石油地球物理勘探, 45(2):208-214.
朱凯光, 王凌群, 谢宾等. 2013. 基于主成分分析的航空电磁数据噪声去除方法. 中国有色金属学报, 23(9):2430-2435.