An automatic preselection strategy for magnetotelluric single-site data processing based on linearity and polarization direction

Frontiers in Earth Science

Earth Science

Hui Cao¹  Gang Wang²  Lili Zhang³  ZhengYong Ren⁴  Hao Chen⁴ 

[1] College of Geophysics, Chengdu University of Technology, Chengdu, China;Energy and Deep Earth Exploration Laboratory, Institute of Geophysical and Geochemical Exploration, China Geological Survey, Langfang, China;Key Laboratory of Petroleum Resource Research, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China;School of Geosciences and Info-Physics, Central South University, Changsha, China;

关键词: magnetotelluric impedance;    linearity;    polarization direction;    data processing;    preselection;   

DOI  :  10.3389/feart.2023.1230071

 received in 2023-05-28, accepted in 2023-08-08,  发布年份 2023

来源: Frontiers

【 摘 要 】

The magnetotelluric response function can be severely disturbed by cultural electromagnetic noise. The preselection strategy is one of the effective ways to remove the influence of noise when calculating the response function. This study proposed three new parameters (the amplitude ratio predicted amplitude ratio and linear coherence (PLcoh) between the predicted and observed electric fields and the dispersion degree of the magnetic polarization direction (DDpol)) to detect noisy data, making the preselection strategy automatic. The first two were used to evaluate the linearity of binary linear regression to constrain incoherent noise, while the last was used to evaluate the magnetic polarization direction to constrain coherent noise. Finally, the technique is illustrated by applying it to two field datasets and comparing it with the previous studies. The results showed that these parameters can be used to effectively identify contaminated data, and a reliable response function can be obtained by using these parameters to extract high-quality data when intermittent noise contaminates field data.

【 授权许可】

Unknown   
Copyright © 2023 Chen, Zhang, Ren, Cao and Wang.