## 1 Introduction  
Magnetotellurics (MT) is an electromagnetic exploration technique that obtains the electrical characteristics of subsurface materials at various depths by studying the Earth's response to naturally occurring alternating electromagnetic fields. This method infers the distribution of the conductivity and resistivity of underground materials based on electromagnetic signals present in nature. Due to its advantages of easy implementation, deep probing capabilities, and a wide frequency range, it has been widely applied in various fields such as oil and gas exploration, geothermal surveys, seismic monitoring, and deep geological structure studies [1]. However, since MT signals are weak and have a broad bandwidth, the data collected in the field are inevitably affected by various types of electromagnetic noise. With the acceleration of industrialization, the noise environment has become increasingly complex, particularly in areas with strong interference, such as mining sites and urban regions, where the noise amplitude can often be several times or even several orders of magnitude greater than natural signals [2]. Furthermore, this noise is typically caused by human activities or the process of urbanization, which this paper refers to as strong cultural noise. Such noise severely distorts the apparent resistivity and phase curves, making it difficult to separate the noise from the natural signals, thus greatly impacting the accuracy of subsurface electrical structure interpretations and the reliability of the collected data. Consequently, removing strong cultural noise interference from MT signals and improving the quality of MT data have become an important research topic in this field [3]. Modern signal processing methods have been widely applied in the field of noise suppression in MT signals. Techniques such as wavelet transform [4], empirical mode decomposition [5], mathematical morphology filtering [6], compressive sensing reconstruction algorithms [7], and sparse decomposition methods [8] have been shown to be effective in improving data quality. Analyzing relevant literature from both domestic and international sources reveals that while the cross-power spectrum and weighted average power spectrum methods possess some capabilities to suppress uncorrelated noise, electromagnetic noise often affects all channels of electromagnetic signals simultaneously, making it correlated noise and rendering these methods ineffective. Although the distant reference method can eliminate uncorrelated electromagnetic noise between different channels at the same measurement point and effectively suppress correlated electromagnetic noise from the same source, it increases the error bars of single-point data to varying degrees after processing, and selecting the distance for the reference channel poses practical challenges. The robust processing method eliminates non-Gaussian noise in electromagnetic signals, effectively reducing the dispersion of apparent resistivity and phase curve data; however, it cannot remove the interference from nearby electromagnetic-correlated noise when the noise level is high and the energy is strong. Although the wavelet transform can effectively suppress locally correlated noise, it is overly reliant on the choice of wavelet basis functions. As the scale increases, the spectral locality of the orthogonal basis functions deteriorates, limiting the detailed decomposition of the MT signals. In recent years, the Hilbert-Huang transform has been widely applied in the field of MT, effectively suppressing power frequency interference in electromagnetic signals. Compared to wavelet transforms, the Hilbert-Huang transform does not require the selection of basis functions and exhibits stronger time-frequency characterization capabilities [9].