汤井田^1,2,,
李广^1,2,3,,,
周聪^1,2,
任政勇^1,2,
肖晓^1,2,
刘子杰⁴
1. 中南大学地球科学与信息物理学院, 长沙 410083
2. 有色金属成矿预测与地质环境监测教育部重点实验室(中南大学), 长沙 410083
3. 东华理工大学省部共建核资源与环境国家重点实验室培育基地, 南昌 330013
4. 核工业二三O研究所, 长沙 410011

基金项目: 国家高技术研究发展计划（863计划）（2014AA06A602），有色金属成矿预测与地质环境监测教育部重点实验室开放基金（2017YSJS09），中国博士后科学基金（2016M602431）联合资助


详细信息

作者简介: 汤井田, 男, 1965年生, 教授, 博导.主要从事电磁法数据处理及正反演研究.E-mail:jttang@mail.csu.edu.cn

通讯作者: 李广, 男, 1988年生, 博士, 讲师.主要从事电磁法数据处理与嵌入式仪器仪表研究.E-mail:li_guangg@163.com

中图分类号: P631

收稿日期:2017-08-06
修回日期:2018-01-17
上线日期:2018-09-05

Denoising AMT data based on dictionary learning

TANG JingTian^1,2,,
LI Guang^1,2,3,,,
ZHOU Cong^1,2,
REN ZhengYong^1,2,
XIAO Xiao^1,2,
LIU ZiJie⁴
1. Institute of Geosciences and Info-Physics, Central South University, Changsha 410083, China
2. Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Central South University), Ministry of Education, Changsha 410083, China
3. State Key Laboratory Breeding Base of Nuclear Resources and Environment, East China University of Technology, Nanchang 330013, China
4. Research Institute No.230, CNNC, Changsha 410011, China


More Information

Corresponding author: LI Guang,E-mail:li_guangg@163.com

MSC: P631

--> Received Date: 06 August 2017
Revised Date: 17 January 2018
Available Online: 05 September 2018


摘要
摘要:音频大地电磁（Audio Magnetotelluric，AMT）信号常常受到持续性人文噪声影响，这类噪声使用远参考法和Robust阻抗估计等常规方法往往效果不佳.为此，本文从噪声的规律与特征出发，提出一种新的AMT数据处理方法.首先通过字典学习方法从观测数据中自主学习到人文噪声的特征结构，构建冗余字典，然后利用学习到的冗余字典，分离出AMT数据中的人文噪声.为验证方法的有效性，首先进行了合成数据的仿真试验，然后在四川凉山进行了针对性的野外试验研究，最后将本文方法应用于庐枞矿集区实测数据的处理.结果表明，本文方法能够快速、准确地分离出AMT信号中的人文干扰，保留有用信号，显著改善AMT数据质量.
关键词: 音频大地电磁勘探/
数据处理/
信噪分离/
字典学习/
移不变稀疏编码

Abstract:The problem that audio magnetotelluric (AMT) data is susceptible to cultural noise is far from being solved. Especially when contaminated by persistent cultural noise, the use of remote reference, robust estimate or other conventional methods can hardly acquire a good result. To remove the persistent cultural noise, this paper proposes a novel method based on dictionary learning. First of all, the features of the cultural noises are learned from observational data through a dictionary learning algorithm, and then the cultural noises are separated by using the learned dictionary. We apply our procedure to synthetic and real data with strong cultural noise and compare different processing methods to that proposed in this paper. As a conclusion, the presented method can quickly and accurately extract the strong cultural noise from raw AMT data and preserve the useful signal completely, and the results acquired by using the filtered data are improved significantly with respect to previous work.
Key words:Audio magnetotelluric sounding/
Data processing/
Signal-noise separation/
Dictionary learning/
Shift invariant sparse coding



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