Extraction Of Multimodal Dispersion Curves From Ambient Noise With Compressed Sensing

We propose a compressed sensing (CS) method for extracting multimodes from ambient noise. We solve the CS inverse problem by using two methods: an l(1)-based optimization algorithm and a Bayesian method. Synthetic and field data examples are conducted to validate our method. The dispersion curves extracted by our method are consistent with those extracted by the widely used frequency-Bessel transform (F-J) method, but our method is more efficient and can extract higher-resolution spectrograms than the F-J method. Our method can quickly and reliably extract multimodes from ambient noise, thereby facilitating studies of ambient noise tomography.