## 6 Conclusions  
To address the issue of significant strong cultural noise interference in geomagnetic time-domain sequence signals--such as impulse, square wave, triangular wave, and periodic noises--which leads to a decline in the quality of apparent resistivity and phase curve data, this paper combines artificial intelligence with the VMD method to develop the PSO-optimized VMD method to process geomagnetic time series signals containing strong cultural noise. Through simulation experiments on four types of typical strong cultural noise signals, the paper discusses two parameters, NCC and SNR, to comprehensively evaluate the denoising performance of this method. The results indicate that, compared to traditional algorithms such as EMD, ITD, and VMD, the NCC and SNR of the PSO-optimized VMD method for suppressing strong cultural noise increased by 0.024, 0.035, 0.019, and 2.225, 2.446, 1.964, respectively. This method effectively extracts the profile characteristics of strong cultural noise while removing impulse, square wave, triangular wave, and periodic noises from the geomagnetic time series signals. Additionally, it significantly preserves the original useful signals during the denoising process. The processing results for the field data demonstrate that this method effectively improves the apparent resistivity and phase curves affected by strong cultural noise, making the curves smoother and more continuous, with a notable enhancement in the quality of low-frequency data and a partial recovery of the curve shapes.  
It is important to note that the various overlapping noises in areas with strong cultural noise interference complicate the time series waveforms. Currently, no algorithm exists that can effectively identify a signal-to-noise ratio that is suitable for all noise waveforms. The manual selection of data not only consumes a significant amount of time and effort but also introduces subjective judgment into the process, lacking a reliable and convenient signal-to-noise ratio identification step. Furthermore, this method does not account for the correlation between electrical and magnetic channels. Therefore, in the face of the complex electromagnetic interference environment in areas with strong cultural noise interference, how to effectively utilize advanced technologies such as artificial intelligence for adaptive signal-to-noise ratio identification, further improve the data quality in low-frequency ranges, and enhance the precision of signal-to-noise separation will be key research directions for the future, providing accurate electromagnetic exploration data for geological interpretation.  
Conceptualization, Z.S. and S.Y.; methodology, Z.S.; software, Z.S. and X.Z.; validation, Z.S. and X.Z.; formal analysis, Z.S.; investigation, Z.S., S.Y. and K.Z.; resources, Z.S.; data curation, K.Z.; writing--original draft preparation, Z.S.; writing--review and editing, Z.S. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors thank [PERSON] from Shanxi Institute of Geological Survey Co., Ltd., and [PERSON] from Shanxi Third Geological Engineering Survey Institute Co., Ltd., for their help in the investigation.  
The authors declare no conflicts of interest.