Hankel function-based Radon transform for high-resolution dispersion imaging

ABSTRACT Space constraints in active surface wave surveys often lead to a limited aperture length and close source-to-receiver spacing, hindering mode separation and amplifying near-field effects. These factors distort the phase velocity estimates, reducing the accuracy of dispersion imaging and subsequent shear wave velocity estimation. Traditional wavefield transformation techniques, including ω-c transform, ω-k transform, τ-p transform, high-resolution linear Radon transform, and frequency-domain beamforming techniques, assume planar wavefronts and fail to account for near-field effects. On the other hand, the cylindrical frequency-domain beamforming method can only partially eliminate near-field effects. It struggles with mode separation, especially in cases where multiple modes are closely spaced due to shifts in energy peaks and finite-aperture limitations. Therefore, addressing near-field effects and mode separation is crucial for improving dispersion imaging. To overcome these challenges, a high-resolution Hankel function-based Radon transform (HRHRT) was proposed. The proposed method incorporates cylindrical weights to compensate for radiation damping and uses a tailored forward projection operator to address model incompatibility effects. Within a weighted preconditioned conjugate gradient least-squares framework, it effectively mitigates near-field effects while enhancing mode separation by reducing energy smearing and refining spectral power around modal peaks. The algorithm is rigorously tested on published synthetic and real-field data sets across diverse subsurface conditions. Synthetic results demonstrate the superiority of HRHRT over traditional wavefield transformation techniques by suppressing near-field effects at lower frequencies more effectively. Furthermore, inversion results show that HRHRT consistently yields the lowest mean absolute percentage error in shear wave velocity estimation compared to other methods. Application to real data sets confirms its ability to enhance the resolution of closely spaced modes. Consequently, HRHRT significantly improves the accuracy of predicted VS profiles from multichannel analysis of surface waves tests, making itself as a valuable advancement in surface wave dispersion imaging.