Rayleigh-wave multicomponent cross-correlation-based source strength distribution inversion. Part 1: Theory and numerical examples

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

doi:10.1093/gji/ggz261

Cited by 28 publications

(39 citation statements)

References 58 publications

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“…This allows us to reuse existing simulations, for example, the generating wavefields for source inversions. This partly mimics the approach taken by Ermert et al () and Xu et al ().…”

Section: Theorymentioning

confidence: 60%

“…We only consider recordings of the vertical displacement. Xu et al () and Wang et al () specifically consider radial‐ and transverse‐component correlations, and Paitz et al () provide a framework for different physical quantities.…”

Section: Theorymentioning

confidence: 99%

“…Inspired by helioseismology (Duvall Jr et al, ; Gizon & Birch, ; Woodard, ), Tromp et al () introduced a new approach — in the following referred to as full‐waveform ambient noise inversion — that acknowledges the fact that both the distribution of ambient noise sources and structure shape interstation correlations. Although Basini et al () followed with a comprehensive sensitivity analysis using observations, subsequent studies only used synthetic tests to improve our intuition for the physics behind correlation functions (Hanasoge, , ; Fichtner, ; Fichtner et al, ; Sager, Boehm, et al, ; Sager, Ermert, et al, ; Xu et al, ). Only recently, real data applications started to emerge, but they keep the structural model fixed and focus on the distribution of ambient noise sources (Datta et al, ; Ermert et al, ).…”

Section: Introductionmentioning

confidence: 99%

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Global‐Scale Full‐Waveform Ambient Noise Inversion

et al. 2020

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We present the first application of full-waveform ambient noise inversion to observed correlation functions that jointly constrains 3-D Earth structure and heterogeneous noise sources. For this, we model and interpret ambient noise correlations as recordings of correlation wavefields, which completely eliminates the limiting assumptions of Green's function retrieval, such as equipartitioning and homogeneous random noise sources. Our method accounts for seismic wave propagation physics in 3-D heterogeneous and attenuating media and also for the heterogeneous and nonstationary nature of the ambient noise field. Designed as a proof of concept, the study considers long periods from 100 to 300 s, thus focusing on the Earth's hum. Treating correlations as self-consistent observables allows us to make separate measurements on the causal and acausal branches of correlation functions, without any need to choose one of them or form the average. We validate our approach by assessing the quality of the obtained models and by comparing them to previous studies. This work is a step toward the establishment of full-waveform ambient noise inversion as a tomographic technique with the goal to go beyond ambient noise tomography based on Green's function retrieval.

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“…This allows us to reuse existing simulations, for example, the generating wavefields for source inversions. This partly mimics the approach taken by Ermert et al () and Xu et al ().…”

Section: Theorymentioning

confidence: 60%

Section: Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Global‐Scale Full‐Waveform Ambient Noise Inversion

et al. 2020

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“…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).…”

mentioning

confidence: 99%

“…Several state-of-the-art open-source tools for ambient noise data processing are freely available, e.g., Whisper (https://codewhisper.isterre.fr/, May 12, 2020), MSnoise (Lecocq et al, 2014), FastPCC (Ventosa et al, 2019), yam (https://github.com /trichter/yam, May 12, 2020), NoisePy (https://github.com/chengxinjiang/Noise_python, May 12, 2020) and DSurfTomo (Fang et al, 2015). However, the same cannot be said about cross-correlation modeling tools, which have mostly been developed ad hoc by different research groups (Hanasoge, 2013a;Fichtner, 2014;Sager et al, 2020;Xu et al, 2019). An exception is the openly available implementation of noise cross-correlations and sensitivity kernels in SPECFEM3D (Tromp et al, 2010); however, in its current form it is not tailored to the exploration of different noise source models and their impact on crosscorrelation.…”

mentioning

confidence: 99%

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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