Generation of Rayleigh-wave dispersion images from multichannel seismic data using sparse signal reconstruction

The accurate estimation of dispersion curves has been a key issue for ensuring high quality in geophysical surface wave exploration. Many studies have been carried out on the generation of a high-resolution dispersion image from array measurements. In this study, the sparse signal representation and reconstruction techniques are employed to obtain the high resolution Rayleigh-wave dispersion image from seismic wave data. First, a sparse representation of the seismic wave data is introduced, in which the signal is assumed to be sparse in terms of wave speed. Then, the sparse signal is reconstructed by optimization using l1-norm regularization, which gives the signal amplitude spectrum as a function of wave speed. A dispersion image in the f-v domain is generated by arranging the sparse spectra for all frequency slices in the frequency range. Finally, to show the efficiency of the proposed approach, the Surfbar-2 field test data, acquired by B. Luke and colleagues at the University of Nevada Las Vegas, are analysed. By comparing the real-field dispersion image with the results from other methods, the high mode-resolving ability of the proposed approach is demonstrated, particularly for a case with strongly coherent modes.