Premium
Extraction of Multimodal Dispersion Curves From Ambient Noise With Compressed Sensing
Author(s) -
Gao Lina,
Zhang Wenqiang,
Zhang Zhenguo,
Chen Xiaofei
Publication year - 2021
Publication title -
journal of geophysical research: solid earth
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.983
H-Index - 232
eISSN - 2169-9356
pISSN - 2169-9313
DOI - 10.1029/2020jb021472
Subject(s) - ambient noise level , noise (video) , compressed sensing , computer science , inverse problem , spectrogram , dispersion (optics) , artificial intelligence , algorithm , computer vision , acoustics , mathematics , image (mathematics) , physics , optics , mathematical analysis , sound (geography)
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.