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Detection of lowermost mantle heterogeneity using seismic migration of diffracted S-waves
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  • Jonathan Wolf,
  • Ed Ganero,
  • Benjamin Schwarz,
  • Yantao Luo,
  • Regina Maass,
  • John D. West
Jonathan Wolf
UC Berkeley

Corresponding Author:[email protected]

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Ed Ganero
Arizona State University
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Benjamin Schwarz
Fraunhofer Institute for Wind Energy and Energy System Technology - Fraunhofer IWES
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Yantao Luo
Yale University
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Regina Maass
Dublin Institute for Advanced Studies
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John D. West
Arizona State University
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Abstract

The bottom of Earth’s mantle hosts strong seismic wave speed heterogeneities, such as ultra-low velocity zones (ULVZs). ULVZ heterogeneities are commonly detected via forward modeling of seismic waveforms, which can include time-consuming waveform synthesis and visual inspection. Furthermore, ULVZ imaging has been most commonly carried out with waves that have limited global coverage. In this work, we investigate the efficacy of the diffracted S (Sdiff) wavefield, which has global coverage to map CMB heterogeneity. We implement a Kirchhoff migration algorithm to objectively investigate the presence or absence of postcursors to Sdiff due to ULVZ heterogeneity. The Kirchhoff approach is efficient, taking less than one CPU-minute per earthquake (for ~1000 receivers) for our implementation. Our approach makes use of the expected moveout of ULVZ-born Sdiff post cursors as a function of distance from great-circle path at the base of the mantle. We investigate epicentral distances 95°, where Sdiff includes asymptotic S and ScS up to diffraction. We test the algorithm using synthetic waveforms calculated for models that include lowermost mantle wave speed heterogeneity. These results demonstrate that the migration approach can well resolve the location of heterogeneity structures in the azimuthal direction, but is less accurate at constraining the along-great circle path location in the absence of crossing ray paths. Lastly, our real-data examples detect CMB heterogeneity that agrees with past ULVZ work. Our algorithm provides a quantitative assessment of the magnitude of the waveform anomalies and, therefore, how anomalous the structures are that are producing them.
24 Jul 2024Submitted to ESS Open Archive
29 Jul 2024Published in ESS Open Archive