2018
DOI: 10.1029/2018gl078610
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Teleseismic Traveltime Tomography of Northern Sumatra

Abstract: We imaged the mantle structure beneath northern Sumatra by inverting high‐quality seismic arrival time data and using a newly developed eikonal equation‐based teleseismic tomography method. Traveltime differences between neighboring stations were reliably extracted by cross‐correlating teleseismic waveforms, which were recorded by 26 stations from January 2009 to January 2018. Both P and S wave tomographic results show the oblique subduction of the Indo‐Australian oceanic lithosphere beneath the Sunda plate. T… Show more

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Cited by 39 publications
(71 citation statements)
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“…After filtering the waveform data within the frequency range from 0.02 to 0.2 Hz, the cross‐correlation traveltime differences between all possible station pairs were measured. In teleseismic traveltime tomography, it is always assumed that the heterogeneity outside the study domain is negligible (Liu et al, ; Rawlinson et al, ). To satisfy this assumption, every station pair must be sufficiently close.…”
Section: Methodsmentioning
confidence: 99%
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“…After filtering the waveform data within the frequency range from 0.02 to 0.2 Hz, the cross‐correlation traveltime differences between all possible station pairs were measured. In teleseismic traveltime tomography, it is always assumed that the heterogeneity outside the study domain is negligible (Liu et al, ; Rawlinson et al, ). To satisfy this assumption, every station pair must be sufficiently close.…”
Section: Methodsmentioning
confidence: 99%
“…All the tomographic results discussed in the subsequent sections consist of relative velocity perturbation, that is, the difference between M b and M 0 divided by M 0 . The least square problem for both regional and teleseismic traveltime tomography is expressed as follows (Liu et al, ; Puspito et al, ): F()X=AXbTCd1()italicAXb+εXTCm1X, where A is the sensitivity matrix, X is the discrete model perturbation vector, C d is the a priori data covariance matrix, C m is the a priori model covariance matrix, and ε is the damping factor for velocity inversion. In regional traveltime tomography, b represents the traveltime difference vector; similarly, b denotes the vector containing relative traveltime differences in teleseismic traveltime tomography.…”
Section: Methodsmentioning
confidence: 99%
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