Magnetotelluric is one of the mainstream exploration geophysical methods, which plays a vital role in studying deep geological structures and finding deep hidden blind ore bodies. The seriousness of human electromagnetic noise causes a large number of abnormal waveforms in the time series of measured magnetotelluric data, and the data can no longer objectively reflect the underground electrical distribution. In this work, we propose a magnetotelluric time series data processing method based on K singular value decomposition dictionary training. First, a training matrix and a to‐be‐processed matrix are built with the pending magnetotelluric signals. Then, let the K singular value decomposition dictionary training process the training matrix to obtain an over‐complete dictionary reflecting the characteristics of the pending signal. Lastly, orthogonal matching pursuit is combined with an over‐complete dictionary updated in real time to sparsely represent the to‐be‐processed matrix and remove human electromagnetic interference in the signal. Experimental results show that the method can update the over‐complete dictionary in real‐time according to the pending magnetotelluric signals, realize the self‐learning signal–noise separation of magnetotelluric signals, and effectively retain low‐frequency information. Compared with method of directions dictionary learning, remote reference method, and orthogonal matching pursuit method, the reconstructed data of the proposed method can more accurately reflect the underground electrical structure information.

中文翻译：

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使用K‐SVD词典训练对大地电磁数据进行去噪

大地电磁是主流勘探地球物理方法之一，在研究深部地质构造和发现深部隐伏的矿体方面起着至关重要的作用。人为电磁噪声的严重性会在测得的大地电磁数据的时间序列中导致大量异常波形，并且该数据不再能够客观地反映地下电气分布。在这项工作中，我们提出了一种基于K奇异值分解字典训练的大地电磁时间序列数据处理方法。首先，利用待处理的大地电磁信号建立训练矩阵和待处理矩阵。然后，让K奇异值分解字典训练过程处理训练矩阵以获得反映待处理信号特征的不完整字典。最后，正交匹配追踪与实时更新的完整字典相结合，以稀疏表示待处理矩阵并消除信号中的人为电磁干扰。实验结果表明，该方法能够根据待处理的大地电磁信号实时更新超完备字典，实现大地电磁信号的自学习信噪分离，并有效地保留了低频信息。与方向字典学习法，远程参考法和正交匹配追踪法相比，该方法的重建数据可以更准确地反映地下电气结构信息。