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

Abstract

Abstract. We introduce 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 pre-eminent seismological observables, in light of non-uniform ambient seismic sources. Written in the Python language, it is both accessible for usage and further development, as well as 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 the output of 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.

Rapid finite-frequency microseismic noise source inversion at regional to global scales

“…The concept of modelling cross-correlations for arbitrary noise source distributions originated in helioseismology (Woodard 1997) and has been modified for the use on Earth by several authors (Tromp et al 2010;Hanasoge 2013b;Fichtner 2014). To provide the necessary context, we give a short derivation of the cross-correlation wavefield equations, similar to Ermert et al (2017Ermert et al ( , 2020. Subsequently, we describe our approach to make the computation feasible for global, high-frequency problems using pre-computed wavefields and spatially variable grids.…”

“…Nishida & Fukao 2007;Tromp et al 2010;Hanasoge 2013a;Ermert et al 2017;Sager et al 2018a;Datta et al 2019;Xu et al 2019), based on concepts originally developed in helioseismology (Woodard 1997;Gizon & Birch 2002). This approach naturally yields noise source sensitivity kernels that may be used to infer the spatial distribution of sources, while honouring the physics of wave propagation through a 3-D heterogeneous medium (Ermert et al 2017(Ermert et al , 2020). Another approach that has been shown to be effective to model ambient noise correlations is normal mode summation (e.g.…”

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

“…Code and data availability. The Python code can be downloaded from GitHub (https://github.com/lermert/noisi, Ermert and Igel, 2020). A tutorial in the form of a Jupyter Notebook is provided as the main item of documentation and details each step for the computation of cross-correlations and sensitivity kernels.…”

Rapid finite-frequency microseismic noise source inversion at regional to global scales

“…The concept of modelling cross-correlations for arbitrary noise source distributions originated in helioseismology (Woodard 1997) and has been modified for the use on Earth by several authors (Tromp et al 2010;Hanasoge 2013b;Fichtner 2014). To provide the necessary context, we give a short derivation of the cross-correlation wavefield equations, similar to Ermert et al (2017Ermert et al ( , 2020. Subsequently, we describe our approach to make the computation feasible for global, high-frequency problems using pre-computed wavefields and spatially variable grids.…”

“…Nishida & Fukao 2007;Tromp et al 2010;Hanasoge 2013a;Ermert et al 2017;Sager et al 2018a;Datta et al 2019;Xu et al 2019), based on concepts originally developed in helioseismology (Woodard 1997;Gizon & Birch 2002). This approach naturally yields noise source sensitivity kernels that may be used to infer the spatial distribution of sources, while honouring the physics of wave propagation through a 3-D heterogeneous medium (Ermert et al 2017(Ermert et al , 2020. To reduce computational cost, particularly when higher frequencies are involved, (Ermert et al 2017) proposed an implementation based on pre-computed wavefields from numerical wavefield solvers such as AxiSEM (Nissen-Meyer et al 2014) and SpecFEM (Komatitsch & Tromp 2002).…”