Extracting surface waves, hum and normal modes: time-scale phase-weighted stack and beyond - INSU - Institut national des sciences de l'Univers Access content directly
Journal Articles Geophysical Journal International Year : 2017

Extracting surface waves, hum and normal modes: time-scale phase-weighted stack and beyond

Sergi Ventosa
  • Function : Author
Martin Schimmel
  • Function : Author

Abstract

Stacks of ambient noise correlations are routinely used to extract empirical Green's functions (EGFs) between station pairs. The time-frequency phase-weighted stack (tf-PWS) is a physically intuitive nonlinear denoising method that uses the phase coherence to improve EGF convergence when the performance of conventional linear averaging methods is not sufficient. The high computational cost of a continuous approach to the time-frequency transformation is currently a main limitation in ambient noise studies. We introduce the time-scale phase-weighted stack (ts-PWS) as an alternative extension of the phase-weighted stack that uses complex frames of wavelets to build a time-frequency representation that is much more efficient and fast to compute and that preserve the performance and flexibility of the tf-PWS. In addition, we propose two strategies: the unbiased phase coherence and the two-stage ts-PWS methods to further improve noise attenuation, quality of the extracted signals and convergence speed. We demonstrate that these approaches enable to extract minor- and major-arc Rayleigh waves (up to the sixth Rayleigh wave train) from many years of data from the GEOSCOPE global network. Finally we also show that fundamental spheroidal modes can be extracted from these EGF.
Fichier principal
Vignette du fichier
ggx284.pdf (2.68 Mo) Télécharger le fichier
Origin : Publisher files allowed on an open archive

Dates and versions

insu-03748838 , version 1 (10-08-2022)

Licence

Attribution

Identifiers

Cite

Sergi Ventosa, Martin Schimmel, Eleonore Stutzmann. Extracting surface waves, hum and normal modes: time-scale phase-weighted stack and beyond. Geophysical Journal International, 2017, 211, pp.30-44. ⟨10.1093/gji/ggx284⟩. ⟨insu-03748838⟩
66 View
22 Download

Altmetric

Share

Gmail Facebook X LinkedIn More