Research of a new algorithm on denoising frequency domain magnetotelluric data based on morphological clustering

被引：0
|
作者：
Guo R. [1 ,2 ,3 ]
Tong W. [1 ,2 ]
Liu J. [1 ,2 ,3 ]
Wang Y. [1 ,2 ]
Yin Z. [1 ,2 ]
Chen H. [1 ,2 ]
机构：
[1] Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Central South University, Ministry of Education, Changsha
[2] Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration, Central South University, Changsha
[3] School of Geosciences and Info-Physics, Central South University, Changsha
来源：
Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) | 2023年 / 54卷 / 10期
基金：
中国国家自然科学基金;
关键词：
clustering; Fourier transform; magnetotelluric method; signal processing;
D O I：
10.11817/j.issn.1672-7207.2023.10.015
中图分类号：
学科分类号：
摘要：
The magnetotelluric (MT) method is a geophysical method that uses natural electromagnetic sources for deep geoelectric structure exploration, but its signals are susceptible to various noise interferences. To better identify the MT signals and noise, a method for automatic clustering was proposed based on the MT signal patterns in the frequency domain, and the algorithm was used to calculate with synthetic and measured data. The results show that in the frequency domain, MT signal patterns have some similarities, and strong interference noise has specific characteristics different from the signal. Such characteristics can be distinguished using cosine similarity, and the signal can be separated from the noise using a clustering algorithm. The apparent resistivity and phase curves calculated by the new algorithm are smoother and more continuous, indicating a significant improvement in MT impedance estimation by using the improved method. © 2023 Central South University of Technology. All rights reserved.
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页码：3937 / 3948
页数：11
相关论文
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