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Seismic imaging of the laterally varying D″ region beneath the Cocos Plate
Author(s) -
Thorne Michael S.,
Lay Thorne,
Garnero Edward J.,
Jahnke Gunnar,
Igel Heiner
Publication year - 2007
Publication title -
geophysical journal international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.302
H-Index - 168
eISSN - 1365-246X
pISSN - 0956-540X
DOI - 10.1111/j.1365-246x.2006.03279.x
Subject(s) - geology , seismogram , tomography , discontinuity (linguistics) , seismic tomography , reflector (photography) , mantle (geology) , core–mantle boundary , seismology , geometry , geophysics , optics , physics , mathematics , mathematical analysis , light source
SUMMARY We use an axisymmetric, spherical Earth finite difference algorithm to model SH ‐wave propagation through cross‐sections of laterally varying lower mantle models beneath the Cocos Plate derived from recent data analyses. Synthetic seismograms with dominant periods as short as 4 s are computed for several models: (1) a D″ reflector 264 km above the core–mantle boundary with laterally varying S ‐wave velocity increases of 0.9–2.6 per cent, based on localized structures from a 1‐D double‐array stacking method; (2) an undulating D″ reflector with large topography and uniform velocity increase obtained using a 3‐D migration method and (3) cross‐sections through the 3‐D mantle S ‐wave velocity tomography model TXBW. We apply double‐array stacking to assess model predictions of data. Of the models explored, the S ‐wave tomography model TXBW displays the best overall agreement with data. The undulating reflector produces a double Scd arrival that may be useful in future studies for distinguishing between D″ volumetric heterogeneity and D″ discontinuity topography. Synthetics for the laterally varying models show waveform variability not observed in 1‐D model predictions. It is challenging to predict 3‐D structure based on localized 1‐D models when lateral structural variations are on the order of a few wavelengths of the energy used, particularly for the grazing geometry of our data. Iterative approaches of computing synthetic seismograms and adjusting model characteristics by considering path integral effects are necessary to accurately model fine‐scale D″ structure.

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