Magnetotelluric (MT) sounding data are easily contaminated by various noise sources, especially noise with a long duration (even full-time noise), which makes it difficult to obtain accurate values when calculating the weight factors of response function, resulting in distortion of the response results. Based on blind source separation theory, fast independent component analysis (FastICA) can separate this kind of noise. However, this method is challenged by the number of separated field sources and the unequal signal amplitude before and after processing. We have developed a novel signal noise separation method, improving the traditional FastICA, where a correlation coefficient is used to compute the number of field sources, and the fast iterative shrinkage threshold algorithm (FISTA) is used to adjust the signal amplitude problem before and after FastICA decomposition. Compared with common field source division methods and the traditional FastICA, the experimental results indicate that our method can separate and remove noise with a long duration, increase the signal-to-noise ratio (SNR) of the data, and improve the MT response curves. Meanwhile, case studies of measured data illustrate that our method obtains a more robust MT response than the conventional robust method and FastICA.

中文翻译：

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基于相关系数和快速迭代收缩阈值算法的大地电磁数据快速独立分量分析去噪

大地电磁（MT）测深数据容易受到各种噪声源的污染，特别是持续时间较长的噪声（甚至是全时噪声），使得在计算响应函数的权重因子时难以获得准确的数值，导致响应函数失真响应结果。基于盲源分离理论，快速独立分量分析（FastICA）可以分离这种噪声。然而，这种方法受到分离场源的数量和处理前后信号幅度不等的挑战。我们开发了一种新颖的信号噪声分离方法，改进了传统的 FastICA，其中使用相关系数来计算场源的数量，并使用快速迭代收缩阈值算法（FISTA）来调整FastICA分解前后的信号幅度问题。实验结果表明，与常见的场源划分方法和传统的FastICA相比，我们的方法可以分离和去除持续时间较长的噪声，提高数据的信噪比（SNR），改善MT响应曲线。 . 同时，测量数据的案例研究表明，我们的方法比传统的鲁棒方法和 FastICA 获得了更鲁棒的 MT 响应。并改善 MT 响应曲线。同时，测量数据的案例研究表明，我们的方法比传统的鲁棒方法和 FastICA 获得了更鲁棒的 MT 响应。并改善 MT 响应曲线。同时，测量数据的案例研究表明，我们的方法比传统的鲁棒方法和 FastICA 获得了更鲁棒的 MT 响应。