Magnetotelluric (MT) method is widely used for revealing deep electrical structure. However, natural MT signals are susceptible to cultural noises. In particular, the existing data-processing methods usually fail to work when MT data are contaminated by persistent or coherent noises. To improve the quality of MT data collected with strong ambient noises, we propose a novel time-series editing method based on the improved shift-invariant sparse coding (ISISC), a data-driven machine learning algorithm. First, a redundant dictionary is learned autonomously from the raw MT data. Second, cultural noises are reconstructed using the learned dictionary and the orthogonal matching pursuit (OMP) algorithm. Finally, the de-noised MT data are obtained by subtracting the reconstructed cultural noises from the raw MT data. The synthetic data, field experimental data and measured data are tested to verify the effectiveness of the newly proposed method. The results show that our new scheme can effectively remove strong cultural noises and has better adaptability and efficiency than the predefined dictionary-based methods. The method can be used as an alternative when a remote reference station is not available.

中文翻译：

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用于大地电磁数据噪声衰减的改进的移不变稀疏编码

大地电磁（MT）方法被广泛用于揭示深部电结构。然而，自然 MT 信号容易受到文化噪音的影响。特别是，当 MT 数据受到持续或相干噪声的污染时，现有的数据处理方法通常无法正常工作。为了提高在强环境噪声下收集的 MT 数据的质量，我们提出了一种基于改进的平移不变稀疏编码 (ISISC) 的新型时间序列编辑方法，这是一种数据驱动的机器学习算法。首先，从原始 MT 数据中自主学习冗余字典。其次，使用学习字典和正交匹配追踪 (OMP) 算法重建文化噪声。最后，通过从原始 MT 数据中减去重建的文化噪声获得去噪 MT 数据。合成数据，现场实验数据和实测数据进行了测试，以验证新提出的方法的有效性。结果表明，与预定义的基于字典的方法相比，我们的新方案可以有效去除强烈的文化噪音，并且具有更好的适应性和效率。当远程参考站不可用时，该方法可用作替代方法。