Ambient seismic noise imaging of the lowermost mantle beneath the North Atlantic Ocean

Retailleau, Lise; Boué, Pierre; Li, Lei; Campillo, Míchel

doi:10.1093/gji/ggaa210

Cited by 27 publications

(23 citation statements)

References 83 publications

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“…The presented developments are therefore worthwhile contributions that deserve further research efforts. A promising direction for further application is to extend the proposed framework to correlation-based deep Earth seismology, either using ocean microseism (Boué et al 2014;Retailleau et al 2020) or late earthquake coda waves (Pha . m et al 2018;Tkalčić et al 2020;.…”

Section: Discussionmentioning

confidence: 99%

Modeling P waves in seismic noise correlations: Advancing fault monitoring using train traffic sources

Sager¹,

Tsai²,

Sheng³

et al. 2022

Preprint

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The theory of Green's function retrieval essentially requires homogeneously distributed noise sources. Even though these conditions are not fulfilled in nature, low-frequency (<1 Hz) surface waves generated by ocean-crust interactions have been used successfully to image the crust with unprecedented spatial resolution. In contrast to low-frequency surface waves, high-frequency (>1 Hz) body waves have a sharper, more localized sensitivity to velocity contrasts and temporal changes at depth. In general, their retrieval using seismic interferometry is challenging, and recent studies focus on powerful, localized noise sources. They have proven to be a promising alternative but break the assumptions of Green's function retrieval. In this study, we present an approach to model correlations between P waves for these scenarios and analyze their sensitivity to 3D Earth structure. We perform a series of numerical experiments to advance our understanding of these signals and prepare for an application to fault monitoring. In the considered cases, the character of the signals strongly diverges from Green's function retrieval, and the sensitivity to structure has significant contributions in the source direction. An accurate description of the underlying physics allows us to reproduce observations made in the context of monitoring the San Jacinto Fault in California using train-generated seismic waves. This approach provides new perspectives for detecting and localizing temporal velocity changes previously unnoticed by commonly exploited surface-wave reconstructions.

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Section: Discussionmentioning

confidence: 99%

Modeling P waves in seismic noise correlations: Advancing fault monitoring using train traffic sources

Sager¹,

Tsai²,

Sheng³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

ModellingPwaves in seismic noise correlations: advancing fault monitoring using train traffic sources

Sager

Tsai

Sheng

et al. 2021

Geophysical Journal International

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Summary The theory of Green’s function retrieval essentially requires homogeneously distributed noise sources. Even though these conditions are not fulfilled in nature, low-frequency (<1 Hz) surface waves generated by ocean-crust interactions have been used successfully to image the crust with unprecedented spatial resolution. In contrast to low-frequency surface waves, high-frequency (>1 Hz) body waves have a sharper, more localized sensitivity to velocity contrasts and temporal changes at depth. In general, their retrieval using seismic interferometry is challenging, and recent studies focus on powerful, localized noise sources. They have proven to be a promising alternative but break the assumptions of Green’s function retrieval. In this study, we present an approach to model correlations between P waves for these scenarios and analyze their sensitivity to 3D Earth structure. We perform a series of numerical experiments to advance our understanding of these signals and prepare for an application to fault monitoring. In the considered cases, the character of the signals strongly diverges from Green’s function retrieval, and the sensitivity to structure has significant contributions in the source direction. An accurate description of the underlying physics allows us to reproduce observations made in the context of monitoring the San Jacinto Fault in California using train-generated seismic waves. This approach provides new perspectives for detecting and localizing temporal velocity changes previously unnoticed by commonly exploited surface-wave reconstructions.

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“…The cross-correlation of the ambient seismic wavefield data (often referred to as “ambient noise”) has been shown to be complementary to earthquake data for imaging the Earth’s structure through surface seismic waves 21 . More recently, the cross-correlation technique has been used successfully to extract body waves and information about the deep structure of the Earth 22 – 24 . This technique relies on the hypothesis that the sources of ambient noise are equipartitioned.…”

Section: Introductionmentioning

confidence: 99%

Multi-phase seismic source imprint of tropical cyclones

Retailleau

Gualtieri

2021

Nat Commun

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The coupling between the ocean activity driven by winds and the solid Earth generates seismic signals recorded by seismometers worldwide. The 2–10 s period band, known as secondary microseism, represents the largest background seismic wavefield. While moving over the ocean, tropical cyclones generate particularly strong and localized sources of secondary microseisms that are detected remotely by seismic arrays. We assess and compare the seismic sources of P, SV, and SH waves associated with typhoon Ioke (2006) during its extra-tropical transition. To understand their generation mechanisms, we compare the observed multi-phase sources with theoretical sources computed with a numerical ocean wave model, and we assess the influence of the ocean resonance (or ocean site effect) and coastal reflection of ocean waves. We show how the location and lateral extent of the associated seismic source is period- and phase-dependent. This information is crucial for the use of body waves for ambient noise imaging and gives insights about the sea state, complementary to satellite data.

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Ambient seismic noise imaging of the lowermost mantle beneath the North Atlantic Ocean

Cited by 27 publications

References 83 publications

Modeling P waves in seismic noise correlations: Advancing fault monitoring using train traffic sources

Modeling P waves in seismic noise correlations: Advancing fault monitoring using train traffic sources

ModellingPwaves in seismic noise correlations: advancing fault monitoring using train traffic sources

Multi-phase seismic source imprint of tropical cyclones

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