Research on High-Density Discrete Seismic Signal Denoising Processing Method Based on the SFOA-VMD Algorithm

With the increasing demand for precision in seismic exploration, high-resolution surveys and shallow-layer identification have become essential. This requires higher sampling frequencies during seismic data acquisition, which shortens seismic wavelengths and enables the capture of high-frequency signals to reveal finer subsurface structural details. However, the insufficient sampling rate of existing petroleum instruments prevents the effective capture of such high-frequency signals. To address this limitation, we employ high-frequency geophones together with high-density and high-frequency acquisition systems to collect the required data. Meanwhile, conventional processing methods such as Fourier transform-based time–frequency analysis are prone to phase instability caused by frequency interval selection. This instability hinders the accurate representation of subsurface structures and reduces the precision of shallow-layer phase identification. To overcome these challenges, this paper proposes a denoising method for high-sampling-rate seismic data based on Variational Mode Decomposition (VMD) optimized by the Starfish Optimization Algorithm (SFOA). The denoising results of simulated signals demonstrate that the proposed method effectively preserves the stability of noise-free regions while maintaining the integrity of peak signals. It significantly improves the signal-to-noise ratio (SNR) and normalized cross-correlation coefficient (NCC) while reducing the root mean square error (RMSE) and relative root mean square error (RRMSE). After denoising the surface mountain drilling-while-drilling signals, the resulting waveforms show a strong correspondence with the low-velocity zone interfaces, enabling clear differentiation of shallow stratigraphic distributions.