Application of Adaptive VMD Algorithm in High-Speed Rail Seismic Signal Extraction

When a high-speed train is in motion, it causes vibrations in the railway tracks, resulting in seismic waves. However, the actual collected data also includes background noise from the Earth and various human activities. Therefore, extracting the high-speed rail seismic signals from the actual recordings is crucial for effectively utilizing this type of signal. In this abstract, we propose an adaptive Variational mode decomposition (VMD)-based separation algorithm for high-speed rail seismic signals. The optimization algorithm is introduced into the variational mode decomposition, and the sample entropy and energy difference parameter are used to construct the fitness function to achieve optimal adjustment of the modal number and penalty factor. In addition, we perform time-frequency analysis on the extracted high-speed rail signals and the field data using the synchrosqueezed wavelet transform (SSWT). The processing and analysis results of the field data show that the algorithm can effectively extract high-speed rail seismic signals and eliminate other ambient noises, which provides the basis for the imaging and inversion of high-speed rail seismic waves.