Introducing noisi: a Python tool for ambient noise cross-correlation modeling and noise source inversion - INSU - Institut national des sciences de l'Univers Access content directly
Journal Articles Solid Earth Year : 2020

Introducing noisi: a Python tool for ambient noise cross-correlation modeling and noise source inversion

Laura Ermert
  • Function : Author
Jonas Igel
  • Function : Author
Korbinian Sager
  • Function : Author
Tarje Nissen-Meyer
  • Function : Author
Andreas Fichtner
  • Function : Author

Abstract

We introduce the open-source tool noisi for the forward and inverse modeling of ambient seismic cross-correlations with spatially varying source spectra. It utilizes pre-computed databases of Green's functions to represent seismic wave propagation between ambient seismic sources and seismic receivers, which can be obtained from existing repositories or imported from the output of wave propagation solvers. The tool was built with the aim of studying ambient seismic sources while accounting for realistic wave propagation effects. Furthermore, it may be used to guide the interpretation of ambient seismic auto- and cross-correlations, which have become preeminent seismological observables, in light of nonuniform ambient seismic sources. Written in the Python language, it is accessible for both usage and further development and efficient enough to conduct ambient seismic source inversions for realistic scenarios. Here, we introduce the concept and implementation of the tool, compare its model output to cross-correlations computed with SPECFEM3D_globe, and demonstrate its capabilities on selected use cases: a comparison of observed cross-correlations of the Earth's hum to a forward model based on hum sources from oceanographic models and a synthetic noise source inversion using full waveforms and signal energy asymmetry.
Fichier principal
Vignette du fichier
se-11-1597-2020.pdf (4.12 Mo) Télécharger le fichier
Origin : Publisher files allowed on an open archive

Dates and versions

insu-03748805 , version 1 (10-08-2022)

Licence

Attribution

Identifiers

Cite

Laura Ermert, Jonas Igel, Korbinian Sager, Eléonore Stutzmann, Tarje Nissen-Meyer, et al.. Introducing noisi: a Python tool for ambient noise cross-correlation modeling and noise source inversion. Solid Earth, 2020, 11, pp.1597-1615. ⟨10.5194/se-11-1597-2020⟩. ⟨insu-03748805⟩
85 View
43 Download

Altmetric

Share

Gmail Facebook X LinkedIn More