Source-distribution estimation from direct Rayleigh waves in multicomponent crosscorrelations

Xu, Zongbo; Mikesell, T. Dylan; Gribler, Gabriel

doi:10.1190/segam2018-2997342.1

Cited by 5 publications

(10 citation statements)

References 15 publications

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“…Throughout this study, we simply treat the negative time derivative of NCFs as EGFs and ignore the influence of unevenly distributed noise sources on the retrieval of EGFs (e.g., Basini et al, 2013;Ermert et al, 2017; 10.1029/2018JB017020 Stehly et al, 2006;Wang et al, 2014Wang et al, , 2016. It has been demonstrated that the uneven distribution of noise sources can introduce biases on the retrieved EGFs (e.g., Fichtner, 2014;Tsai, 2009), the extent of which has been estimated by a number of studies based on either analytic methods (e.g., Ermert et al, 2015;Froment et al, 2010;Xu et al, 2018;Yao & Van Der Hilst, 2009) or full wave numerical simulations (e.g., Fichtner, 2014;Tromp et al, 2010;Sager et al, 2017). Fichtner (2014) suggests that carrying out full waveform ANT without taking source heterogeneities and data processing schemes into account could introduce tomographic artifacts.…”

Section: Discussionmentioning

confidence: 99%

Three‐Dimensional Sensitivity Kernels for Multicomponent Empirical Green's Functions From Ambient Noise: Methodology and Application to Adjoint Tomography

2019

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Adjoint tomography has recently been applied to ambient noise data as a new and promising tomographic method that utilizes simulation‐based 3‐D sensitivity kernels rather than ray theory used in traditional ambient noise tomography. However, to date, most studies of ambient noise adjoint tomography only use vertical‐component Rayleigh waves. In this study, we develop a theoretical framework for calculating sensitivity kernels for multicomponent empirical Green's functions extracted from ambient noise data. Under the framework of the adjoint method, we demonstrate that a horizontal component (transverse‐transverse or radial‐radial) kernel can be constructed from the interaction of wave fields generated by point‐force sources acting in the north and east directions based on rotation relationships. Our method is benchmarked for a 3‐D heterogeneous isotropic model by comparing rotated seismograms, individual, and event traveltime misfit kernels with corresponding references computed by numerical simulations with sources directly placed in the radial or transverse directions. Based on our new method, we perform the first Love‐wave ambient noise adjoint tomography in southern California and construct an improved VSH model. Our method for computing sensitivity kernels of multicomponent empirical Green's functions provides the basis for multicomponent ambient noise adjoint tomography in imaging radially anisotropic shear‐wave velocity structures.

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Section: Discussionmentioning

confidence: 99%

Three‐Dimensional Sensitivity Kernels for Multicomponent Empirical Green's Functions From Ambient Noise: Methodology and Application to Adjoint Tomography

2019

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“…power spectral densities of seismic noise, can be modeled for arbitrary noise source distributions. Finally, it can be utilized for noise source inversion when no updates to the Earth structure model are required, similar to the pioneering study by Nishida and Fukao (2007), who inverted observed cross-correlations for source distribution of the Earth's hum, and as performed by Ermert et al (2017); Xu et al (2018Xu et al ( , 2019 and Datta et al (2019).…”

Section: Possible Applicationsmentioning

confidence: 95%

“…To deal with sources of unknown, stochastic phase, it is commonly assumed that they are spatially uncorrelated when averaged over a sufficiently long observation span, or that their correlation length is far below observational resolution (e.g. Snieder, 2004;Nishida and Fukao, 2007;Tromp et al, 2010;Stutzmann et al, 2012;Hanasoge, 2013b;Farra et al, 2016;Xu et al, 2018;Datta et al, 2019). Upon this assumption, the noise sources can be described by their location-dependent power spectral density (PSD):…”

Section: Cross-correlation Modelingmentioning

confidence: 99%

“…While the number of applications based on the Green's function assumption is large and rapidly increasing (Nakata et al, 2019), only a modest number of studies have presented models of ambient noise crosscorrelations themselves, i.e. numerical evaluations of cross-correlations due to distributed noise sources, rather than models of Green's functions (e.g., Nishida and Fukao, 2007;Tromp et al, 2010;Hanasoge, 2013a;Basini et al, 2013;Ermert et al, 2017;Sager et al, 2018b;Datta et al, 2019;Xu et al, 2018Xu et al, , 2019Sager et al, 2020).…”

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confidence: 99%

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

Ermert

Igel

Sager

et al. 2020

Preprint

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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.

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“…Numerical models of noise auto-and crosscorrelations allow us to probe this assumption and eventually circumvent it (Halliday and Curtis, 2008;Fan andSnieder, 1598 L. Ermert et al: Noise modeling andinversion 2009;Cupillard and Capdeville, 2010;Kimman and Trampert, 2010;Fichtner, 2014;Stehly and Boué, 2017;Delaney et al, 2017). While the number of applications based on the Green's function assumption is large and rapidly increasing (Nakata et al, 2019), only a modest number of studies have presented models of ambient noise cross-correlations themselves, i.e., numerical evaluations of cross-correlations due to distributed noise sources rather than models of Green's functions (e.g., Nishida and Fukao, 2007;Tromp et al, 2010;Hanasoge, 2013a;Basini et al, 2013;Ermert et al, 2017;Sager et al, 2018bSager et al, , 2020Datta et al, 2019;Xu et al, 2018Xu et al, , 2019.…”

Section: Motivationmentioning

confidence: 99%

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

et al. 2020

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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.

show abstract

Source-distribution estimation from direct Rayleigh waves in multicomponent crosscorrelations

Cited by 5 publications

References 15 publications

Three‐Dimensional Sensitivity Kernels for Multicomponent Empirical Green's Functions From Ambient Noise: Methodology and Application to Adjoint Tomography

Three‐Dimensional Sensitivity Kernels for Multicomponent Empirical Green's Functions From Ambient Noise: Methodology and Application to Adjoint Tomography

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

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

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