High-Resolution Directional Passive Surface Waves Dispersion Imaging Based on Smoothing MUSIC

Abstract

The passive surface wave dispersion imaging is extensively utilized for shallow surface velocity inversion. However, the presence of strong directional noise sources often leads to deviations from the truth dispersion. Conventional beamforming technique can correct dispersion spectrum, but with limited resolution. Additionally, actual records contain random noise, which further compromises imaging quality. To address these challenges concerning dispersion imaging resolution and noise resistance, we propose a high-resolution dispersion imaging method that integrates the multiple signal classification (MUSIC) algorithm with subarray spatial smoothing processing. Initially, velocity is incorporated into the MUSIC algorithm to discern the direction of ambient noise, thereby extracting a sparse f–v spectrum free from random noise interference. To further mitigate the impact of random noise, a subarray spatial-smoothing MUSIC approach is devised, effectively reducing such interferences. Synthetic and field experiments demonstrate its capability to achieve high-resolution dispersion spectrum even in the presence of noise.