Denoising Application of Magnetotelluric Low-Frequency Signal Processing

奇异值分解 计算机科学 信号处理 算法 噪音(视频) 降噪 矩阵分解 频域 大地电磁法 分段 信号(编程语言) 希尔伯特-黄变换 模式识别(心理学) 人工智能 数学 白噪声 数字信号处理 计算机视觉 工程类 计算机硬件 图像(数学) 电气工程 物理 数学分析 电信 特征向量 电阻率和电导率 量子力学 程序设计语言
作者
Jin Li,Fanhong Ma,Jingtian Tang,Yecheng Liu,Jin Cai
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing [Institute of Electrical and Electronics Engineers]
卷期号:60: 1-18 被引量:8
标识
DOI:10.1109/tgrs.2022.3210334
摘要

As magnetotelluric (MT) is an important method for exploring the geoelectrical structure of the underground, it has motivated in-depth research and application by many geophysicists. Nevertheless, due to the influence of the environment, the collected data are interfered with strong humanistic noise, which might result in a loss of their authenticity. To solve the above problems, many time-frequency domain methods have emerged. Based on the advantages of singular value decomposition (SVD) denoising, we propose a method of magnetotelluric noisy data processing based on multiresolution singular value decomposition (MSVD) and improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), which overcomes the lack of flexibility in the construction of the matrix in SVD data processing. First, we introduce a new signal processing method by generalizing SVD to MSVD to obtain more accurate signal characteristics. Due to the difficulty of matrix selection, we suggest the singular value contribution rate as the standard to determine the suitable Hankel matrix and use MSVD to perform effective decomposition. Second, we propose the ICEEMDAN algorithm for removing impulse noise, which efficiently processes each modal component through adaptively decomposition of different thresholds. Experiments on synthetic and realistic data demonstrate that our proposed method can separate the large-scale contours of the magnetotelluric noisy data and improve the time-domain waveform quality of low-frequency signal. The apparent resistivity-phase curves, coherence and SNR are all obviously promoted.
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