Extraction of multimodal dispersion curves from ambient noise with
Although higher-mode surface wave dispersion curves can provide
additional constraints on the subsurface velocity structure, their
extraction from ambient noise data remains more intractable than the
extraction of fundamental-mode dispersion curves. Recently, the
frequency-Bessel transform (F-J) method was developed to extract
multimodal dispersion curves from ambient noise. Here, we propose an
alternative compressed sensing (CS) method for extracting multimodes
from ambient noise. We solve the CS inverse problem by using two
methods: an l1-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 F-J method, but our method is more efficient and can
extract higher-resolution dispersion energy images than the F-J method.
Our method can quickly and reliably extract multimodes from ambient
noise, thereby facilitating studies of ambient noise tomography.